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Disaster management system necessitates efficient and resilient communication networks to ensure effective emergency response and recovery efforts. Disasters pose significant challenges to communication infrastructures, often leading to breakdowns in communication networks and disrupting rescue and relief efforts. In recent years, metaheuristic algorithms have emerged as a promising solution for optimizing various aspects of disaster scenarios. In this paper, we investigate the use and application of metaheuristic algorithms for addressing various optimization problems that arise during emergency operations. The key challenges in disaster management design, including victim localization, routing, coverage, and resource allocation, are discussed. This study also discusses the strengths and limitations of different metaheuristic algorithms for disaster scenarios. Finally, it highlights recently developed models and future research directions in the application of metaheuristic algorithms for disaster area network optimization.
Department of Computer Science and Engineering, National Institute of Technology, Dimapur, Nagaland, India
Lithungo K. Murry*
Department of Computer Science and Engineering, National Institute of Technology, Dimapur, Nagaland, India
*Address all correspondence to: lithungo@nitnagaland.ac.in
1. Introduction
Natural and man-made disasters affect millions of people globally, often resulting in loss of human life and significant damage to infrastructure and property. In recent years, the frequency and severity of such events have highlighted the critical importance of effective disaster management strategies. Most disasters are unforeseen occurrences that cause large numbers of fatalities, property damage, devastation of the environment, and significant effects on society [1]. In the event of such disasters, the ground network infrastructure and road connectivity frequently experience significant disruptions, making it extremely difficult to conduct efficient search and rescue missions [2]. When a disaster occurs, the most critical challenge is to promptly establish initial communication with affected victims who are trapped or inaccessible to rescue teams. Emergency responders can act quickly and effectively when they have efficient emergency management strategies and response protocols, which can help prevent a crisis from escalating [3]. Designing a suitable model for rescuer assignment is crucial to efficiently allocate rescuers to evacuees injured in real-time, considering the dynamic and hazardous environment. However, these tasks are NP-hard because of the dynamic environments and complex relationships between pedestrian movement and information dissemination, which involve minimizing the overall potential cost of the rescue operation [4]. In recent times, researchers have explored metaheuristic algorithms for disaster management, showing significant promise in addressing challenges in disaster applications.
Optimization, a field at the intersection of mathematics and computer science, explores methodologies and techniques for discovering the optimal solution to a given optimization problem. Applying mathematical programming approaches to large-scale problems can be challenging and time-consuming. In response, over the past decades, numerical optimization techniques, particularly metaheuristic algorithms, have gained widespread adoption for addressing real-world problems [5].
Metaheuristic algorithms (MAs) are a subset of heuristic methods, employ iterative processes that explore the entire problem space using various heuristic approaches. In contrast to traditional algorithms, metaheuristic algorithms are usually inspired by theoretical and empirical sources, they frequently mimic biological or natural phenomena [6]. These algorithms are flexible enough to be designed, integrated, or adjusted to fit particular problem domains. By utilizing stochastic techniques, these algorithms methodically navigate intricate search spaces, offering benefits related to resilience and worldwide search abilities [7]. The challenges in disaster management are classified as localization, routing, coverage, and resource allocation. These challenges can be defined as optimization problems, where researchers seek to achieve the best possible results with limited resources. The objective functions can either be optimally maximized or minimized. Various types of objectives for disaster management are illustrated in Figure 1.
Figure 1.
Various types of objectives for disaster management system.
This study aims to present a thorough analysis of metaheuristic algorithms developed for disaster management system. The paper begins by presenting an overview of MAs and explored the problems involved in emergency and relief operations using MAs. Furthermore, this work provides the strengths and limitations of employing metaheuristic algorithms in disaster management. Additionally, recent optimization strategies and hybrid techniques developed using MAs for disaster management are also included in the study. We explore the current status of MAs in disaster management and discuss future directions that may be explored. This paper highlights the significance of MAs for emergency and disaster management by providing insights into the benefits and challenges of MAs.
In the following sections of the paper, we first classify metaheuristic algorithm types into five categories, as outlined in Section 2. Following this, we explore the recent studies in MAs designed to address post-disaster challenges, which are elaborated in Section 3, and hybrid optimization algorithms in Section 4. Section 5 provides the limitations and discussion of the literature review. Section 6 describes the benefits and limitations. Finally, Section 7 discusses the conclusion and future challenges uncovered through this study.
Metaheuristics are algorithmic frameworks used across various optimization problems, often requiring minimal modifications to adapt to specific problem instances. In this section, MAs are classified into evolutionary algorithm (EA), physical-based algorithm (PA), nature-inspired algorithm (NA), swarm-based algorithm (SA), and miscellaneous algorithm (MA) as shown in Figure 2.
Figure 2.
Classifications of metaheuristic algorithms.
2.1 Evolutionary algorithm
Evolutionary algorithms (EAs) represent a class of modern heuristic search techniques, leveraging the concept of “survival of the fittest” from natural evolution. These algorithms mimic natural processes like genetic inheritance and the Darwinian struggle for survival [8]. Evolutionary-based metaheuristic algorithms, such as genetic algorithms (GA) [9], differential evolution (DE) [10], evolutionary strategies (ES) [11], evolutionary programming (EP) [12], and genetic programming (GP) [13], are inspired by principles of natural evolution and genetics. In disaster area network, EAs can be used to optimize resource allocation strategies and routing protocols for communication networks. Moreover, EAs can handle multi-objective optimization problems effectively, balancing multiple criteria such as minimizing response time, maximizing resource utilization, and minimizing costs.
The EAs optimization process typically involves initializing a population of potential solutions, evaluating each solution’s fitness based on an objective function, selecting candidates for reproduction based on their fitness, and creating offspring through recombination or crossover. Offspring then replace some candidates in the current population, maintaining a constant population size. This iterative process continues until a termination criterion is met, such as reaching an optimal solution or running out of computational resources. The steps involved in EAs are shown in Figure 3.
Figure 3.
Steps involve in evolutionary algorithm.
EAs maintain a population of potential solutions, inspiring diversity and reducing the likelihood of getting trapped in local optima. It is crucial for disaster relief operations where multiple feasible strategies may need to be evaluated.
2.2 Physical-based algorithms
Physical metaheuristic algorithms are a class of optimization techniques inspired by physical processes and phenomena found in nature. These algorithms simulate physical processes to guide the search for optimal solutions in complex optimization scenarios. The primary key sources of inspiration for this subcategory are gravity, the big bang, black holes, galaxies, and fields [14]. Some popular physical metaheuristic algorithms include harmony search (HS) [15], simulated annealing (SA) [16], gravitational search algorithm (GSA) [17], Black Hole Algorithm (BHA) [18], memetic algorithm (MA) [19], and shuffle frog-leaping algorithm (SFA) [20]. Physical-based optimization can optimize the positioning of communication nodes or facility locations by considering factors such as population distribution, accessibility, terrain features, and infrastructure availability. Moreover, these techniques can dynamically distribute resources to regions with the most critical requirements by continuously monitoring parameters such as signal strength, traffic volume, and changes in network topology.
2.3 Nature-inspired algorithm
Nature-inspired algorithms are computational approaches that draw inspiration from concepts found in biological systems, ecological systems, and evolutionary mechanisms. These techniques are increasingly applied in disaster management to address challenges such as localization, resource allocation, routing, and communication recovery [21]. Bat Algorithm (BA) [22], Cuckoo Search Algorithm (CSA) [23], Gray Wolf Optimizer (GWO) [24], Flower Pollination Algorithm [25], Whale optimization [26], Krill Head Algorithm (KHA) [27], Artificial Immune System (AIS) [28] are some popular nature-inspired algorithms. Several problems have been effectively tackled by these algorithms in a variety of domains, such as engineering, finance, robotics, optimization, and data mining. These algorithms use the inherent intelligence of nature like the swarming behavior of birds or the foraging behavior of ants to provide effective and efficient solutions for complex problems. Moreover, these techniques can optimize resource allocation, load balancing, reduce the effects of disasters, enhance responder coordination and communication, and quickly deliver vital services to impacted populations.
2.4 Swarm intelligence
The term “swarm intelligence” describes the collective behavior of self-organizing, decentralized systems that are modeled after social insect colonies like termites, ants, and bees. Problems like routing, optimization, and decision-making can be resolved by swarm intelligence systems without the need for explicit coordination or centralized control. Many MAs have been influenced by SI; for example, Particle Swarm Optimization (PSO) [29], Ant Colony Optimization (ACO) [30], Glowworm Swarm Optimization (GSO) [31], Artificial Bee Colony (ABC) [32], Fish Swarm Algorithm (FSA) [33], Crow- Search Algorithm (CSA) [34]. Swarm-based optimization algorithms can facilitate optimal resource allocation in disaster areas, including the deployment of Unmanned Aerial Vehicles (UAVs) for relaying messages and establishing ad hoc networks. By considering factors such as network coverage and energy consumption, these algorithms can help maximize communication range and reliability while minimizing resource usage. These algorithms can optimize search patterns, identify high-priority locations based on survivor likelihood, and adapt to dynamic environments.
The SI algorithms involve several key steps as shown in Figure 4. The initial steps involve determining the swarm’s parameters, such as its number of populations, positions, velocities, and other relevant features. The objective function that needs to be optimized is also specified. The fitness of each swarm member is then determined by evaluating its present solution based on this objective function. Following evaluation, individuals adjust their positions or solutions, guided by principles inspired by social insects’ behavior, such as following pheromone trails or social interactions within the neighborhood. Termination criteria are defined to determine when the algorithm should stop its iteration, such as reaching a maximum number of iterations or achieving a certain fitness level. After the termination requirements are met, the final positions or solutions found by the swarm are analyzed to derive insights from the optimization process.
Figure 4.
Steps involve in SI algorithm.
Swarm-based algorithms balance exploitation (using promising solutions) and exploration (widely searching the solution space). In disaster management, this balance is critical because responders must simultaneously investigate different approaches while exploiting effective solutions promptly.
2.5 Miscellaneous algorithms
Similar algorithms draw inspiration from nature, such as artificial intelligence concepts, game theory, human behavior, mathematics, and political strategies. Teaching-Learning Based Optimization (TLBO) [35], Brain Storm Optimization (BSO) [36], Firefly Algorithm (FA) [37], Football Game Inspired Algorithm (FGIA) [38], Search and Rescue Optimization (SRO) [39] have been utilized in disaster management. Applications such as pattern recognition, network routing, clustering, and decision-making have been tackled by these algorithms. These techniques can be effectively applied to address various optimization challenges and enhance network performance in disaster scenarios.
3. Metaheuristic algorithms for disaster management
Metaheuristic algorithms use in disaster management systems is enumerated in this section. The problems are classified under four major areas as follows.
3.1 Localization
One of the most challenging tasks during or after a disaster scenario is localization of victims. Rescue teams can concentrate their efforts more effectively by reducing response times when the precise location of the victim is known. Effective victim localization minimizes the time and resources required for search and rescue operations, as shown in Table 1.
Proposed a localization algorithm that combines boundary-based optimization with PSO to locate target UAV nodes efficiently. This methodology achieves lower localization error and higher localization accuracy by analyzing noise parameters that are uniformly distributed with respect to distance and environment.
Describes swarm interaction and includes artificial potential functions to support swarm attraction toward victims and collision avoidance, considering the complex and hazardous terrain. The outcomes show how the algorithm allows the rescue team to successfully identify possible victims and avoid collisions.
A genetic algorithm-based intelligent search system is utilized to identify signal strength variations between distress signals and drones, improving tracking accuracy.
Devised a range-free distributed localization algorithm for UAV networks. This method incorporates the bounding cube approach to enhance sampling efficiency, thereby reducing the computational load. This approach reduces distance estimation errors and improves efficiency, facilitating rapid convergence.
Proposed a Hierarchical-Reference-PSO to enhance the global search capabilities by evaluating sub-fitness functions of particles. In contrast to traditional PSO, where a combination of local best and global best positions is utilized, HRPSO employs a reference best position to guide particle movements. The suggested approach shows less complexity and localization error, providing a more accurate and efficient solution for UAV localization tasks.
The trilateration method is used to estimate the centroid around a specific target node, and Salp swarms are then implemented around the centroid to improve the accuracy of the solution.
Table 1.
Summary of papers for localization problems using MAs.
3.2 Routing
In disaster environments, certain characteristics such as signal attenuation and short-term communication links among rescue crews contribute to frequent route disruptions as well as unreliable end-to-end services. Efficient routing in disaster-affected networks maximizes the effective use of scarce resources such as battery power, bandwidth, and processing capacity while guaranteeing reliable data transmission between nodes and central points, such as command center or relief coordination center. Table 2 shows a summary of papers addressing routing problem.
An ACO optimization is employed to identify the most efficient routes for firefighting and search and rescue missions within buildings. This method and its variations are highly effective at quickly finding the best routes in networks.
An enhanced Squirrel Search Algorithm (SSA) for multicast routing is proposed by focusing computation only inside the destination-oriented zone, it effectively minimizes computation time while satisfying constraints such as energy, delay, and packet loss.
Devised a hybrid ACO approach where particles serve as operators, expanding the search scope for viable depots and subsequently assigning clients to them. This enables ants to determine the vehicle routes that are most effective and balanced for each selected depot.
An adaptive selection mutation-constrained DE algorithm is presented to select individuals based on their fitness and constraints and explores the most promising candidates among the selected individuals to guide the mutations. This approach maintains exploration capabilities while improving exploitation.
Proposed an enhanced PSO incorporating Variable Neighborhood Search that determines the best next-hop routing depending on factors like distance between nodes and the Base Station (BS) and link reliability, ensuring reliable and timely data transmission to the BS.
A mixed integer linear programming (MILP) model is introduced to optimize the routes for emergency vehicles and minimize both the service completion time and the number of patients experiencing worsened conditions due to delayed medical care.
Develop an enhanced CSO-based multi-hop routing for choosing the optimal path. This method computes fitness functions to increase network lifetime and energy efficiency using a variety of input variables.
Table 2.
Summary of papers for routing problems using MAs.
3.3 Coverage
Coverage extension plays a vital role in disaster management by ensuring that communication networks can reach more affected areas, enhancing the resilience of communication infrastructure during disasters, and facilitating timely response and recovery actions, as shown in Table 3.
Investigates the coverage problem using Ant Colony-Based Scheduling Algorithm. The algorithm uses positional information to assess each sensor’s coverage and initializes the pheromone field based on these assessments. Ants prioritize sensors with more coverage, leveraging sensor capabilities to systematically optimize solutions.
A UAV-Artificial Bee Colony (U-ABC) algorithm is used for determining the optimal flight positions for each UAV-BS, improves coverage and maximize network throughput within disaster areas.
Allows the UAV-BS to find the optimal UAV altitude using PSO by moving straight from its starting position to the ideal position in a single maneuver, improving coverage while consuming less time and energy.
Proposed a Grasshopper Optimization Algorithm (GOA)with the aim of maximizing the information derived from assessing areas and roads while simultaneously maximizing total coverage in damaged areas and roads.
Presented a coverage enhancement strategy utilizing the Ant Lion Optimization (ALO) algorithm by reallocating antlions and dynamically reducing their numbers and a continual randomly generated boundary shrinkage factor is added.
An enhanced PSO model is designed to investigate the most efficient allocation of SAR resources considering various constraints on ship and aircraft base station configurations. The experimental findings indicate that the model effectively optimizes coverage area while allocating maritime rescue resources.
Suggested using K-means initialized GWO to deploy 3D ultra-dense small cells (UBS) in order to maximize the number of user equipment served in the disaster area, guaranteeing the fulfillment of data rate requirements for each UE while ensuring that the number of covered UEs does not exceed each UBS’s capacity limit.
Table 3.
Summary of papers for coverage problems using MAs.
3.4 Resource allocation
Disasters often result in widespread damage and disruption, necessitating the rapid mobilization of resources such as SAR operation, equipment, and supplies to affected areas. Effective resource allocation in disaster management is essential for optimizing response efforts, maximizing efficiency, minimizing losses, enhancing coordination and collaboration, and adapting to dynamic conditions as shown in Table 4.
Introduces an enhanced PSO algorithm for shelter allocation and incorporates SA to improve search efficiency and avoid local optima. In Guangzhou City, China’s Zhuguang Block, the suggested algorithm is demonstrated to be effective in allocating earthquake emergency shelters.
NSGA-II and Modified Multiple-Objective PSO, are suggested to minimize the overall cost and the scarcity of relief supplies while optimizing the distribution of facilities. Data from relief bases in the first area of Tehran city are used to validate the proposed model.
A Multi-Objective Optimization (MOO) problem is defined to identify optimal SAR strategies and efficiently schedule resource allocation. This methodology enables emergency response quickly and efficiently.
Introduces an evolutionary PSO algorithm, incorporating a particle coding scheme and evolutionary operations aligned with the geospatial edge services chain model and presents a fitness evaluation model that considers multiple geospatial edge services characteristics on the edge nodes.
Presents a novel approach for post-disaster emergency resource allocation, using GA to address the uncertainties and complexities that occur in rescue operations.
PSO and variable neighborhood search are suggested in order to tackle the mixed integer programming model focused on time-cost considerations in the presence of uncertainty.
An enhanced PSO algorithm has been introduced, leveraging multi-agent theory and evolutionary population dynamics. This approach optimizes response operations and resource utilization, reducing both time and losses.
Shuffled shepherd optimization (SS0) with a dynamic-window method algorithm is employed to minimize the frequency of recharging or replacement of sensors. This approach considers static and dynamic objects in the environment to prevent collisions and improve energy efficiency during data collection, movements, and transmission.
While traditional DE relies on random search during the mutation stage, leading to limited exploitation capability, an Exponential Selection-based Differential Evolution (ERDE) introduces exponential selection. This strategy improves the direction of mutation and increases the effectiveness of exploitation by increasing the chance of selecting superior individuals.
Multi-objective vibration damping optimization and NSGA-II are used for combined emergency preparedness and response decision-making. The model’s objectives are to arrange emergency shelters or depots in the best possible locations and make it easier for relief vehicles to move between disaster sites and these shelters.
Suggested Elite-preserved Genetic Algorithm (EGA) to optimize the allocation of emergency resources in maritime situations, aiming to enhance efficiency by considering factors such as economic benefits, response time, and safety index.
An enhanced Jellyfish Search Algorithm (JSA), which mimics the foraging behavior of jellyfish and is utilized for quick and effective distribution and logistics. During the jellyfish search process, a Cauchy strategy is employed to determine the internal motion’s moving distance, enhancing global search capabilities and avoiding local optimal solutions.
Proposed a clustering model for optimal target-region allocation using PSO where each affected person is represented as a particle and the fitness function is defined by minimizing the average distance and standard deviation, ensuring that all particles converge to cluster centers by adjusting their positions according to the global best fitness.
Table 4.
Summary of papers for resource allocation problem using MAs.
3.5 Other metaheuristic algorithms
Some new MAs for disaster management are suggested in this section. In recent years, researchers have studied numerous approaches like SRO, TLB0, and Smart Flower Optimization (SFO) and have shown them to be effective and efficient in solving similar kinds of problems, as shown in Table 5. These new metaheuristic algorithms still have scope for improvement, making significant enhancements in disaster applications.
Proposed a novel Swarm Robotics SRO using robots with environmental awareness for search and rescue missions in disaster sites. The locations of the survivors act as the global best solutions, with one robot distinguished as the master and others as slaves. The outcomes show improved convergence speed and quality of the solutions.
Introduced a novel Chaotic SRO-based multi-hop data transmission by incorporating chaotic principles into the conventional SRO algorithm, designed to enhance energy efficiency and prolong the network lifetime.
An innovative approach called Reward-based Reinforcement Learning integrating Particle Swarm Optimization (R-RLPSO) is proposed for real-time rescue operations in 3-D underwater environments and provides cost-effectiveness and quick response.
Presented a new MA for efficient UAV path planning in a shorter period of time. Two growth strategies are used in this approach to control the movement of immature flowers, and particle positions are updated through mathematical modeling.
A discrete-TLBO is employed to minimize the average completion time for all SAR teams. The algorithm creates a two-phase local search method to enhance the solution after producing an initial task scheduling sequence for every rescue team.
Designed a heuristic crossover approach with the fundamental SRO technique to enhance convergence speed while preserving population diversity during the optimization procedure, ensuring high convergence speed and enhancing path planning effectiveness.
Introduces an improved ACO algorithm to enhance the discovery of a mobile search object exhibiting Markovian motion, optimizing search success within a short time and limited resources. This improved algorithm achieves search trajectories with higher success rates in shorter times.
Table 5.
Summary of papers using new MAs for disaster management.
A hybrid optimization algorithm is a technique that combines multiple optimization techniques to solve a given problem. By leveraging the features of different algorithms, hybrid optimization aims to improve solution quality, convergence speed, and robustness, particularly for complex and challenging optimization problems. Table 6 provides a summary of papers using hybrid MAs.
A biogeography-based optimization (BBO) algorithm is integrated with the DE mutation operator to improve exploration capabilities and adopt a local ring topology within the population to prevent premature convergence. This approach reduces the weighted time required to deliver all necessary supplies to the target area.
A multi-objective hybrid DE and PSO algorithm is proposed to generate a set of Pareto solutions aimed at minimizing the use of rescue vehicles as well as the extinguishing time in combating forest fires, considering limited resources.
A hybrid algorithm is designed to address the routing problem of emergency vehicle scheduling for grain transportation by assessing crowd density and performs non-dominated sorting of the population in the ACO phase, minimizing the distribution time of vehicle scheduling.
Employs a hybrid algorithm to investigate the emergency rescue vehicle dispatch problem in order to generate more effective initial solutions that minimize rescue times, delay costs, and maximize lifesaving utility.
Devised an enhanced hybrid method for selecting cluster heads and relay nodes, incorporating a novel fitness function that considers energy efficiency and communication link quality. This approach extends network coverage and network lifetime by optimizing multi-hop routing.
Addresses the routing problem by integrating the Brain Storm Optimization (BSO) algorithm with Large Neighborhood Search (LNS) and simulated SA techniques. BSO generates an initial solution through K-means clustering, where a cluster center is chosen randomly and updated. These solutions are further optimized using LNS, which involves splitting and regrouping individuals using “destroy” and “repair” operators to improve their quality.
Designed a hybrid algorithm to maximize GWO’s exploitation potential where the alpha wolf explores its surroundings, with the beta and delta wolves closely following the alpha’s movements. Moreover, DE gains from a strategically integrated rank-based mutation technique that increases its potential for exploitation.
Table 6.
Summary of papers using hybrid MAs for disaster management.
According to the results of the literature review, MAs are still in the early stages of development in tackling post-disaster problems like localization of victims and coverage extension problems. There has been limited research explored to overcome these challenges. MAs often require careful parameter tuning to achieve optimal performance, a process that can be both time-consuming and problem-specific process. Thus, future research should prioritize long-term disaster recovery, leveraging hybridization and machine learning algorithms.
The common MAs, such as Genetic Algorithm (GA), Simulated Annealing (SA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO), employ strategies that allow for comprehensive and more efficient exploration of solution spaces. These algorithms use mechanisms like randomization and probabilistic decision-making to escape local optima and cover more solution space quickly. GA and PSO can optimize the logistics of resource distribution by finding the best routes and schedules, considering constraints like road conditions and supply availability, and thereby minimizing cost and time. MAs, including ACO and SA, are effective in solving network routing problems and evacuation route planning considering dynamic factors. Some research has developed hybrid approaches by combining various metaheuristic approaches in order to provide better solutions.
Metaheuristic algorithms offer several benefits when applied to disaster scenarios, leading to more effectiveness and efficiency in disaster response and recovery efforts. MAs algorithms can efficiently explore large solution spaces and find near-optimal solutions within reasonable timeframes. This efficiency is crucial in time-sensitive disaster situations where quick decision-making is necessary to save lives and minimize damage. Moreover, MAs can handle noisy, incomplete, or uncertain parameters in disaster scenarios, ensuring reliable decision-making even in challenging conditions. MAs can handle MOO problems by generating a set of Pareto-optimal solutions that represent trade-offs between competing objectives, enabling decision-makers to explore and select the most suitable solutions based on their preferences and priorities.
While metaheuristic algorithms offer promising solutions for optimization problems in disaster management, it also faces several challenges. MAs generally have several parameters that need to be tuned to achieve optimal performance. Finding the right parameter settings for a specific disaster management problem can be challenging and may involve extensive experimentation. Many disaster management problems involve multiple conflicting objectives, such as minimizing cost, maximizing resource utilization, reducing energy, and maximizing coverage. MAs need to balance these conflicting objectives and find a set of solutions that represent trade-offs between them, which can be challenging.
MAs often rely on data to inform decision-making processes. During disaster scenarios, data may be limited, incomplete, or of poor quality due to infrastructure damage, communication breakdowns, or other challenges. As a result, the effectiveness of MAs can be compromised, leading to suboptimal solutions. Moreover, most computational experiments are usually performed on test problems that are generated at random; very few studies use real-world data for testing.
Quick decisions must be made during emergency situations. In order to reduce the effects of disasters and save lives, decision-makers frequently need to act quickly and find solutions in real time. However, many MAs require considerable computational time to converge to a solution, making them less suitable for time-critical decision-making. Different metaheuristic algorithms have varying levels of computational complexity. Some metaheuristic algorithms have complex operations and require a significant number of iterations to converge to adequate solutions. This complexity contributes to higher computational costs, especially when dealing with high-dimensional optimization problems or problems with complex constraints.
Natural disasters pose significant causes of human lives and damage to infrastructure and property. The application of metaheuristic algorithms has increasingly addressed the complexity associated with managing these disasters. This study discusses an overview of recent research developments in metaheuristic optimization for disaster management. We have discussed various emergency and disaster relief problems using MAs and hybrid optimization techniques. Recent research on the utilization of MAs for disaster management offers faster and more effective solutions. Compared to conventional methods, MAs are more effective at quickly exploring vast solution spaces, have inherent adaptability to uncertainty and randomness, are robust against noise, and have scalability to high dimensions. Therefore, they are highly suitable for addressing emergency and disaster relief operations. This study shows the key findings and benefits of MAs in enhancing network coverage, optimizing resource utilization, and minimizing response time and cost in emergency and disaster relief operations.
7.2 Future research directions
Considering the challenges, the following aspects highlight the future development of MAs in disaster management:
Hybrid and adaptive algorithms: Researchers can investigate the development of hybrid optimization tools that combine metaheuristic algorithms with artificial intelligence, aiming to improve solutions and reduce computation time by effectively balancing exploration and exploitation. Hybrid algorithms have proven effective in solving a wide range of planning and scheduling problems, but their potential for use in emergency optimization issues remains to be explored.
Real-world applications and case studies: There are currently very few publicly available benchmark problems in this field. More standard test suites and advanced simulation tools for evaluating and analyzing different MAs must be developed. In order to validate the effectiveness of these algorithms, more case studies and real-world applications should be conducted and using feedback loop mechanism from real-world implementations to continuously improve and adapt the algorithms.
Multi-objective optimization and decision support: The utilization of multi-objective optimization techniques is expected to provide significant benefits for studies focused on optimizing disaster relief operations. Utilizing Pareto fronts to illustrate trade-offs between different objectives so that decision-makers can choose the best-balanced solutions.
The study has identified research gaps and suggests potential future research directions. Additionally, it highlights the necessity for further real-world applications to validate the applicability of these proposed approaches in uncertain emergency scenarios.
The authors state that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Written By
Bidyarani Langpoklakpam and Lithungo K. Murry
Submitted: 12 June 2024Reviewed: 15 June 2024Published: 19 January 2025
Open access peer-reviewed chapter - ONLINE FIRST
