Adaptive multi-swarm in dynamic environments

Qin, Jin; Huang, Chuhua; Luo, Yimo

doi:10.1016/j.swevo.2021.100870

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…7. In Tables 6-8, the symbols -, +, ≈ indicate the results of Wilcoxon rank sum test [34,35] with a significance level of 0.05. Correspondingly, the symbol -indicates that the performance is significantly worse than the proposed method, the symbol + indicates that the performance is significantly better than the proposed method, and the symbol ≈ indicates that the performance difference is not statistically significant compared to the proposed method.…”

Section: Comparison With Other Meteheuristic Methodsmentioning

confidence: 99%

A Hybrid STA Based on Nelder–Mead Simplex Search and Quadratic Interpolation

Zhou

2023

Electronics

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State transition algorithm (STA) is a metaheuristic method for global optimization. However, due to the insufficient utilization of historical information, it still suffers from slow convergence speed and low solution accuracy on specific problems in the later stages. This paper proposes a hybrid STA based on Nelder–Mead (NM) simplex search and quadratic interpolation (QI). In the exploration stage, NM simplex search utilizes the historical information of STA to generate promising solutions. In the exploitation stage, QI utilizes the historical information to enhance the local search capacity. The proposed method uses an eagle strategy to maximize the efficiency and stability. The proposed method successfully combines the merits of the three distinct approaches: the powerful exploration capacity of STA, the fast convergence speed of NM simplex search and the strong exploitation capacity of QI. The hybrid STA is evaluated using 15 benchmark functions with dimensions of 20, 30, 50 and 100. Moreover, the results are statistically analyzed using the Wilcoxon signed-rank sum test. In addition, the applicability of the hybrid STA to solve real-world problems is assessed using the wireless sensor network localization problem. Compared with six state-of-the-art metaheuristic methods, the experimental results demonstrate the superiority and effectiveness of the proposed method.

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Section: Comparison With Other Meteheuristic Methodsmentioning

confidence: 99%

A Hybrid STA Based on Nelder–Mead Simplex Search and Quadratic Interpolation

Zhou

2023

Electronics

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“…As a result, in the early stage, the proportion of leaders performing global search is too low, the proportion of followers is too high, the leader can not effectively carry out global search. However, in the later iteration, the proportion of followers is too low, the local development is not enough, and the precision is not high [11] . In response to this problem, this paper introduces the leader-follower adaptive adjustment strategy [12] .…”

Section: Adaptive Adjustment Strategymentioning

confidence: 99%

A Multi-Strategy Integrated Optimized Salp Swarm Algorithm and Its Application for Engineering Problems.

Huang

2023

HSET

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Salp Swarm Algorithm has the advantages of few adjustment parameters and easy implementation, which has been applied in many fields, such as data mining, image processing, and engineering calculation. However, this algorithm is easy to fall into local optimization and sometimes the accuracy of optimization is not high. To solve the above problems in SSA, this paper proposes an orthogonal opposition-based adaptive slap swarm algorithm OOASSA. First, an orthogonal opposition learning strategy was introduced when updating the leader position to enhance the adequacy of global search and improve the algorithm's ability to get out of the local extreme value. Then, it was introduced adaptive inertia weight when updating the follower position and introduced an adaptive adjustment strategy in the ratio of a leader-follower number to ensure that the algorithm had a good global development ability in the early stage of iteration. Good local exploration ability in late iteration. In this paper, 10 test functions and 3 engineering optimization problems in CEC2017 are used for simulation experiments, and the proposed algorithm's global exploration, local mining, and local optimization capabilities are significantly better than many of the most advanced SSA variants.

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A survey of multi-population optimization algorithms for tracking the moving optimum in dynamic environments

Yazdani,

Blanco-Davis

et al. 2024

J Membr Comput

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The solution spaces of many real-world optimization problems change over time. Such problems are called dynamic optimization problems (DOPs), which pose unique challenges that necessitate adaptive strategies from optimization algorithms to maintain optimal performance and responsiveness to environmental changes. Tracking the moving optimum (TMO) is an important class of DOPs where the goal is to identify and deploy the best-found solution in each environments Multi-population dynamic optimization algorithms are particularly effective at solving TMOs due to their flexible structures and potential for adaptability. These algorithms are usually complex methods that are built by assembling multiple components, each of which is responsible for addressing a specific challenge or improving the tracking performance in response to changes. This survey provides an in-depth review of multi-population dynamic optimization algorithms, focusing on describing these algorithms as a set of multiple cooperating components, the synergy between these components, and their collective effectiveness and/or efficiency in addressing the challenges of TMOs. Additionally, this survey reviews benchmarking practices within this domain and outlines promising directions for future research.

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Adaptive multi-swarm in dynamic environments

Cited by 5 publications

References 31 publications

A Hybrid STA Based on Nelder–Mead Simplex Search and Quadratic Interpolation

A Hybrid STA Based on Nelder–Mead Simplex Search and Quadratic Interpolation

A Multi-Strategy Integrated Optimized Salp Swarm Algorithm and Its Application for Engineering Problems.

A survey of multi-population optimization algorithms for tracking the moving optimum in dynamic environments

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