Ant colony optimization for path planning in search and rescue operations

Morin, Michael; Abi‐Zeid, Irène; Quimper, Claude-Guy

doi:10.1016/j.ejor.2022.06.019

Cited by 39 publications

(13 citation statements)

References 24 publications

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“…When an ant begins to forage for food, it explores randomly, and as it moves, leaves chemical pheromone trails on the ground, which can be detected by other ants. The route with the most intense pheromone scent is more likely to be chosen by other ants [71].…”

Section: Ant Colony Optimization-levy Flight Algorithmmentioning

confidence: 99%

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Güven

2024

IET Control Theory & Appl

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This study elucidates the use of optimization algorithms to identify the controller parameters employed in adjusting the current and voltage values of loads powered by solar energy systems and battery groups. Parameters for these controllers were independently derived using a combination of ant colony optimization with Levy flight, hybrid firefly‐particle swarm optimization, hybrid gravitation search algorithm‐particle swarm optimization, alongside the implementation of Jaya and whale optimization algorithms. The results from each method were juxtaposed for thorough analysis. In addition, three distinct Maximum Power Point Tracker (MPPT) algorithms were employed in the system: perturbation and observation, open circuit voltage, and incremental conductance (IC). To assess the system’s adaptability to real‐world conditions, it was tested against varying temperatures and sunlight levels. Moreover, potential changes in the loads were considered by varying the load. The efficacy of the controllers was examined by altering both the environment and load. The effectiveness of the controllers was examined by referring to the integral of time‐weighted absolute error value. The system was simulated using MATLAB/Simulink software. This study demonstrates that the fractional‐order PID controller achieves the most effective results, the Jaya algorithm provides the best controller parameters, and the IC technique exhibits the highest performance in MPPT.

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Section: Ant Colony Optimization-levy Flight Algorithmmentioning

confidence: 99%

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Güven

2024

IET Control Theory & Appl

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“…In many specific applications, artificial ants can exchange information with each other during local optimization processes, and there are also improved swarm algorithms where artificial ants can perform simple predictions. Due to the parallelism of swarm algorithms, a large number of ants can improve the overall search ability and ensure the stability of the algorithm [16][17]. However, if the number of ants increases to some extent, it would lead to a large amount of information on the previous research path tending to average, and the positive feedback effect of the information is not significant [18][19].…”

Section: Basic Aco and Parameter Analysismentioning

confidence: 99%

Exploration on Ant Colony Optimization for Optimizing Land Use Spatial Allocation

Si,

2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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The purpose of optimizing the allocation of land use (LU) space is to enable effective utilization of land resources in various regions, in order to maximize the economic and social benefits of limited land area. The upper reaches (UR) of the Yellow River are important agricultural production bases and ecological barriers in China, and optimizing land space is of great significance for promoting agricultural development and protecting the ecological environment. Therefore, the optimization method based on ant colony optimization (ACO), as a new type of optimization algorithm, has strong adaptability, robustness, and global search ability, which can effectively solve the problem of LU spatial optimization configuration. This article determined the definition and constraints of land issues, and set relevant parameters. The steps of the ACO were analyzed, and the results of the algorithm were analyzed and evaluated. The LU situation in the UR of the Yellow River was analyzed using survey methods and statistical analysis. The results showed that the patch density index before and after optimization in various regions was generally small, with the index values of forest land and construction land being 0.1142 and 0.2975, respectively.

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“…MILP has multiple applications in problems of importance to society: ambulance relocation (Lee et al, 2022), balanced item placement (Gasse et al, 2022), cost-sharing for ride-sharing (Hu et al, 2021), drop box location (Schmidt and Albert, 2022), efficient failure detection in large-scale distributed systems (Er-Rahmadi and Ma, 2022), home healthcare routing (Dastgoshade et al, 2020), home service routing and appointment scheduling (Tsang and Shehadeh, 2022), inventory control under demand and lead time uncertainty (Thorsen and Yao, 2017), job-shop scheduling (Liu et al, 2021), facility location (Basciftci et al, 2021), flow-shop scheduling (Hong et al, 2019;Balogh et al, 2022;Öztop et al, 2022), freight transportation (Archetti et al, 2021), location and inventory prepositioning of disaster relief supplies (Shehadeh and Tucker, 2022), machine scheduling with sequence-dependent setup times (Yalaoui and Nguyen, 2021), maritime inventory routing (Gasse et al, 2022), multi-agent path finding with conflict-based search (Huang et al, 2021), multi-depot electric bus scheduling (Gkiotsalitis et al, 2023), multi-echelon/multi-facility green reverse logistics network design (Reddy et al, 2022), optimal physician staffing (Prabhu et al, 2021), optimal search path with visibility (Morin et al, 2023), oral cholera vaccine distribution (Smalley et al, 2015), outpatient colonoscopy scheduling (Shehadeh et al, 2020), pharmaceutical distribution (Zhu and Ursavas, 2018), plant factory crop scheduling (Huang et al, 2020), post-disaster blood supply (Hamdan and Diabat, 2020;Kamyabniya et al, 2021), real assembly line balancing with human-robot collaboration (Nourmohammadi et al, 2022), reducing vulnerability to human trafficking (Kaya et al, 2022), restoration planning and crew routing (Morshedlou et al, 2021)...…”

Section: Importance and Difficulties Of Milp Problemsmentioning

confidence: 99%

Lagrangian Relaxation for Mixed-Integer Linear Programming: Importance, Challenges, Recent Advancements, and Opportunities

Bragin¹

2023

Preprint

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Operations in areas of importance to society are frequently modeled as Mixed-Integer Linear Programming (MILP) problems. While MILP problems suffer from combinatorial complexity, Lagrangian Relaxation has been a beacon of hope to resolve the associated difficulties through decomposition. Due to the non-smooth nature of Lagrangian dual functions, the coordination aspect of the method has posed serious challenges. This paper presents several significant historical milestones (beginning with Polyak's pioneering work in 1967) toward improving Lagrangian Relaxation coordination through improved optimization of non-smooth functionals. Finally, this paper presents the most recent developments in Lagrangian Relaxation for fast resolution of MILP problems. The paper also briefly discusses the opportunities that Lagrangian Relaxation can provide at this point in time.

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Ant colony optimization for path planning in search and rescue operations

Cited by 39 publications

References 24 publications

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Exploring solar energy systems: A comparative study of optimization algorithms, MPPTs, and controllers

Exploration on Ant Colony Optimization for Optimizing Land Use Spatial Allocation

Lagrangian Relaxation for Mixed-Integer Linear Programming: Importance, Challenges, Recent Advancements, and Opportunities

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