Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy

Xie, Zixuan; Huang, Xueyu; Liu, Wenwen

doi:10.1155/2022/9599417

Cited by 9 publications

(7 citation statements)

References 43 publications

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“…Levy is a random number obeying the Levy distribution, with the Levy distribution defined by the following equation [55] L (x, γ , µ)…”

Section: B Formula For the New Updated Velocitymentioning

confidence: 99%

Particle Swarm Optimization Algorithm With Adaptive Two-Population Strategy

Zhao

2023

IEEE Access

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The particle swarm optimization (PSO) algorithm is a swarm intelligence (SI) algorithm used to solve optimization problems. Owing to its advantages in simplicity, using only a few parameters, PSO has become one of the most popular optimization algorithms. However, the single structure of PSO leads to challenges in finding the appropriate optima, resulting in low convergence accuracy. To solve the defects of PSO, it is necessary to increase the diversity of the populations involved as well as enhance the ability of the algorithm to develop locally. In this study, we propose a PSO algorithm with an adaptive two-population strategy (PSO-ATPS), which adaptively divides a population into two groups representing excellent and ordinary populations. Inspired by animal hunting behavior, a new velocity-position update method is proposed for the general population. A velocity update formulation with decreasing inertia weights based on logistic chaotic mapping is applied to the excellent population. The algorithm increases the diversity of the population by continuously changing the search strategy of the particles. In addition, a new neighborhood search strategy (oscillation strategy) is proposed, in which a particle searches randomly in its own adaptive neighborhood when its motion is stagnant and updates the particle position using an elite strategy. Among several optimization strategies, PSO-ATPS achieved first place in 7, 8, and 9 groups of tests involving 10 test functions in 3 dimensions, indicating the accuracy and effectiveness of PSO-ATPS. The results show that the performance of PSO-ATPS is competitive, and many improvements developed for PSO can be applied to PSO-ATPS, demonstrating the potential for further development.

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“…Levy is a random number obeying the Levy distribution, with the Levy distribution defined by the following equation [55] L (x, γ , µ)…”

Section: B Formula For the New Updated Velocitymentioning

confidence: 99%

Particle Swarm Optimization Algorithm With Adaptive Two-Population Strategy

Zhao

2023

IEEE Access

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“…Inspired by the fo raging behavior of birds, it is used for various optimization problems, such as benchmark function proble ms, CEC problems, multiple target problem, etc. [37,38]. All particles in the population are affected by the particle's own best historical experience (𝑃𝑏𝑒𝑠𝑡 𝑡 ) and the global best historical experience ( 𝐺𝑏𝑒𝑠𝑡 𝑡 ).…”

Section: Standard Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Coding-Elite Particle Swarm Optimization Searching of Moving Targets by UAVs

Xie

Huang

Liu

et al. 2022

Preprint

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This paper proposes an elite coded particle swarm optimization (CEPSO) unmanned aerial vehicles (UAVs) search target motion for target detection in the shortest time. According to Bayesian theory, the problem of moving object searched is transformed into the optimization of cost function, which is used to describe the probability of moving objects being found. In order to better fit the problem, a coded elite particle swarm optimization algorithm is used to encode the search trajectory of the UAV as the movement path of the particle in the process of generation and evolution. This coding method uses the cognitive properties and social learning properties of particle swarms to generate better solutions. At the same time, in order to increase the diversity of particle swarms, a method of elite particle perturbation is used, which greatly improves the detection probability. In order to verify the superiority of the algorithm in this paper, a large number of simulation experiments are carried out on the existing methods. The experimental results show that compared with PSO, the detection probability of the proposed CEPSO is improved by 6.9%, and the time performance is improved by 8.5%. It also compares with other meta heuristic algorithms, including differential evolution (DE), artificial bee colony (ABC), harmony search (HS), biogeographic-based optimization (BBO), invasive weed optimization (IWO), teaching-learningbased Optimization (TLBO). The experimental results show that the algorithm proposed in this paper has better detection accuracy and less detection time.

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“…Inspired by the foraging behavior of birds, it is used for various optimization problems, such as benchmark function problems, CEC problems, multiple target problem, etc. [37,38]. All particles in the population are affected by the particle's own best historical experience (𝑃𝑃𝑏𝑏𝑃𝑃𝑃𝑃𝑃𝑃 𝑡𝑡 ) and the global best historical experience (𝐺𝐺𝑏𝑏𝑃𝑃𝑃𝑃𝑃𝑃 𝑡𝑡 ).…”

Section: Standard Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Coding-Elite Particle Swarm Optimization Searching of Moving Targets by UAVs

Xie

Huang

Liu

et al. 2022

Preprint

View full text Add to dashboard Cite

This paper proposes an elite coded particle swarm optimization (CEPSO) unmanned aerial vehicles (UAVs) search target motion for target detection in the shortest time. According to Bayesian theory, the problem of moving object searched is transformed into the optimization of cost function, which is used to describe the probability of moving objects being found. In order to better fit the problem, a coded elite particle swarm optimization algorithm is used to encode the search trajectory of the UAV as the movement path of the particle in the process of generation and evolution. This coding method uses the cognitive properties and social learning properties of particle swarms to generate better solutions. At the same time, in order to increase the diversity of particle swarms, a method of elite particle perturbation is used, which greatly improves the detection probability. In order to verify the superiority of the algorithm in this paper, a large number of simulation experiments are carried out on the existing methods. The experimental results show that compared with PSO, the detection probability of the proposed CEPSO is improved by 6.9%, and the time performance is improved by 8.5%. It also compares with other meta-heuristic algorithms, including differential evolution (DE), artificial bee colony (ABC), harmony search (HS), biogeographic-based optimization (BBO), invasive weed optimization (IWO), teaching-learning-based Optimization (TLBO). The experimental results show that the algorithm proposed in this paper has better detection accuracy and less detection time.

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Subpopulation Particle Swarm Optimization with a Hybrid Mutation Strategy

Cited by 9 publications

References 43 publications

Particle Swarm Optimization Algorithm With Adaptive Two-Population Strategy

Particle Swarm Optimization Algorithm With Adaptive Two-Population Strategy

Coding-Elite Particle Swarm Optimization Searching of Moving Targets by UAVs

Coding-Elite Particle Swarm Optimization Searching of Moving Targets by UAVs

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