Sardine Optimization Algorithm with Agile Locality and Globality Strategies for Real Optimization Problems

Zhang, Hongguang; Tang, MengZhen; Liu, Yuanan; Li, Xiang

doi:10.1007/s13369-022-07350-y

Cited by 2 publications

(1 citation statement)

References 49 publications

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“…In this section, compared to the other excellent algorithms of the CEC series, i.e. iCMAES-ILS (ranked second in IEEE CEC2013) [38], SHADE (first ranked non-CMA-ES variant of IEEE CEC2013) [39], LSHADE (winner of CEC2014 competition) [40], EBOwithCMAR (winner of IEEE CEC2017) [41], CJADE (one of the advanced DEs) [42], HSES (winner of IEEE CEC2018) [43], and iLSHADE-RSP (one of the advanced DEs) [44]. The population size and the maximum number of iterations were also set to 40 and 1000, respectively, and the experimental results are recorded in table 7.…”

Section: Comparison With the Winners Of Cec2013-cec2018mentioning

confidence: 99%

An adaptive particle swarm optimization with information interaction mechanism

Liu,

Wei,

Zhang

2024

Mach. Learn.: Sci. Technol.

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The Particle Swarm Optimization (PSO) algorithm is easy to implement owing to its simple framework, and has been successfully applied to many optimization problems. However, the standard PSO easily falls into the local optimum and has weak search ability. To enhance the optimization ability of the algorithm, this paper proposes an adaptive particle swarm optimization with information interaction mechanism (APSOIIM). First, a chaotic sequence strategy was used to produce uniformly distributed particles and enhance their convergence speed at the initialization stage of the algorithm. Then, an interaction information mechanism is introduced to enhance the diversity of the population with the progress of the search, which can effectively interact with the best information of neighboring particles to maintain the balance between exploration and exploitation. Besides, the convergence was proven to verify the robustness and efficiency of the proposed APSOIIM algorithm. Finally, the proposed APSOIIM was applied to solve the CEC2014 benchmark functions and CEC2017 benchmark functions as well as famous engineering optimization problems. The experimental results show that the proposed APSOIIM has significant advantages over the compared algorithms.

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Section: Comparison With the Winners Of Cec2013-cec2018mentioning

confidence: 99%

An adaptive particle swarm optimization with information interaction mechanism

Liu,

Wei,

Zhang

2024

Mach. Learn.: Sci. Technol.

View full text Add to dashboard Cite

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An Improved Sparrow Algorithm Based on Small Habitats in Cooperative Communication Power Allocation

Chen

2023

Electronics

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To solve the power allocation problem of multiple relays in cooperative communication, a sparrow improvement algorithm based on small habitats is proposed. First, the small-habitat rule increases the diversity of the energy classification of communication nodes. The problem of the premature convergence of the algorithm is solved. Subsequently, the joint jump rule is designed to replace the local search rule, and the local search rule of the sparrow algorithm is modified to avoid the local wandering phenomenon. The improved search method solves the local solution problem of the most available function. Lastly, a validation experiment of the performance of the algorithm is carried out according to the IEEE CEC 2017 benchmark document set. The simulation verifies the practicality of various algorithms. The maximum evaluation number (max FE) of the objective function is calculated to compare the performance of various algorithms. The experimental results show that the improved algorithm can increase the diversity of species. The multi-point search capability and global merit search capability are improved. Additionally, the convergence speed and computational accuracy of the algorithm are improved. The results show that the improved method is effective in controlling power in collaborative communication. The energy control algorithm has some practicality. The Friedman and Wilcoxon test criteria are applied and the convergence speed and computational accuracy of the improved algorithm are shown to be higher than those of other algorithms. This indicates that the improved power control method has some practicality in collaborative communication.

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Sardine Optimization Algorithm with Agile Locality and Globality Strategies for Real Optimization Problems

Cited by 2 publications

References 49 publications

An adaptive particle swarm optimization with information interaction mechanism

An adaptive particle swarm optimization with information interaction mechanism

An Improved Sparrow Algorithm Based on Small Habitats in Cooperative Communication Power Allocation

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