A modified symbiotic organisms search algorithm for unmanned combat aerial vehicle route planning problem

Miao, Fahui; Zhou, Yongquan; Luo, Qifang

doi:10.1080/01605682.2017.1418151

Cited by 34 publications

(16 citation statements)

References 29 publications

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“…The most basic relationships that exist in the living organisms are mutualism, commensalism, and parasitism in the environment. Figure 2 illustrates these basic relationships that explained in the following subsections (Miao et al, 2018).…”

Section: Symbiotic Organisms Search Algorithmmentioning

confidence: 95%

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Solving quadratic assignment problem by symbiotic organisms search algorithm

Abdel‐Basset

Rashad

Zhou

2019

IJIE

Self Cite

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Symbiotic organisms search algorithm (SOS) is a new meta-heuristic algorithm based on the symbiotic relationship between the biological, which was proposed in recent years. In this paper, we propose new robust and powerful metaheuristic algorithm called an improved version of symbiotic organisms search algorithm integrated with construction algorithm and mutation operators (ISOS) to solve quadratic assignment problems. The quadratic assignment problem (QAP) considers NP-hard combinatorial optimisation problem and has various practical applications. A set of benchmarks from QAPLIB Library are employed to evaluate the performance of proposed algorithm. ISOS is compared to several algorithms in the literature. The comparisons show that the proposed algorithm shows reliable, efficient and promising results.

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Section: Symbiotic Organisms Search Algorithmmentioning

confidence: 95%

“…SOS was presented by Cheng and Prayogo in 2014 for solving continuous optimisation problems. Then, SOS has shown its proficiency in solving optimisation problems in discrete search space and its variants were proposed by scholars constantly (Abdullahi and Ngadi, 2016;Miao et al, 2018).…”

Section: Minimize a B X Xmentioning

confidence: 99%

Solving quadratic assignment problem by symbiotic organisms search algorithm

Abdel‐Basset

Rashad

Zhou

2019

IJIE

Self Cite

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“…Ezugwu and Adewumi (2017a) proposed a soft set symbiotic organisms search algorithm for optimizing virtual machine resource selection in cloud computing environment. Miao et al (2018) introduced the modified versions of SOS by incorporating the simplex method in the original SOS algorithm to solve the unmanned combat aerial vehicle path planning problem. Saha and Mukherjee (2018) presented a reduced SOS integrated with a chaotic local search to improve the solution accuracy and convergence mobility of the basic SOS.…”

Section: Introductionmentioning

confidence: 99%

A Symbiotic Organisms Search Algorithm With Memory Guidance for Global Optimization Problems

Zhao¹,

Liu²

2021

Preprint

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Symbiotic organisms search (SOS) algorithm is a nature-inspired meta-heuristic algorithm, which has been successfully applied to solve a large number of problems of different areas. In this work, a novel modified variant of SOS with a memory strategy and good-point set (GMSOS) is proposed to improve the properties of exploration and exploitation. For improving the population diversity and the search capability of the SOS algorithm, the good point set theory rather than random selection is used to generate the initial population, and the memory strategy is adopted in three phases of the SOS algorithm, which aims at maintaining the trade-off between exploration and exploitation effectively, and preventing the current best solution from getting trapped into local optima. The performance of the proposed version of SOS is tested on standard benchmark functions with different characteristics and real-world problems. The numerical and statistical results on these applications demonstrate the competitive ability of the proposed algorithm as compared to other popular algorithms available in the literature.

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“…Lim et al tried to solve the constrained path planning of AUVs using a selectively hybridized PSO algorithm, and the proposed algorithm offered good stability and computational efficiency 30 . Miao et al combined a symbiotic organism search algorithm with the simplex method for solving the route planning problem, and the proposed algorithm provided faster convergence speed, higher precision, and stronger robustness 31 . Luo et al proposed a quantum encoding BA to address uninhabited combat aerial vehicle path planning, and the results indicated that the proposed algorithm was an effective and feasible method 32 .…”

Section: Introductionmentioning

confidence: 99%

Two‐dimensional optimal path planning for autonomous underwater vehicle using a whale optimization algorithm

Yan

Zhang

Yang

et al. 2020

Concurrency and Computation

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This paper presents a whale optimization algorithm (WOA) based on forward looking sonar to achieve two‐dimensional optimal path planning for an autonomous underwater vehicle. The purpose of path planning is not only to effectively avoid threat regions and safely reach the intended target with minimum fuel cost but also to obtain an optimal or near‐optimal path in a complex ocean battlefield environment. The WOA, based on the bubble‐net attacking behavior of humpback whales, mimics encircling the prey, attacks with a bubble‐net method, and search for prey to effectively determine the global optimal solution in the search space. The WOA not only has fast convergence speed and high calculation accuracy but can also effectively balance exploration and exploitation to avoid falling into a local optimum and obtain the global optimal solution. Five sets of experiments are applied to verify the superiority and stability of the WOA. Compared with other algorithms, such as artificial bee colony, bat algorithm, cuckoo search, flower pollination algorithm, moth‐flame optimization algorithm, particle swarm optimization, and water wave optimization, the WOA exhibits better optimization performance and stronger robustness. The experimental results reveal that the WOA can find the shortest path compared with all the other algorithms, and it is an effective and feasible method for solving the path planning problem.

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A modified symbiotic organisms search algorithm for unmanned combat aerial vehicle route planning problem

Cited by 34 publications

References 29 publications

Solving quadratic assignment problem by symbiotic organisms search algorithm

Solving quadratic assignment problem by symbiotic organisms search algorithm

A Symbiotic Organisms Search Algorithm With Memory Guidance for Global Optimization Problems

Two‐dimensional optimal path planning for autonomous underwater vehicle using a whale optimization algorithm

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