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. 2022 Dec 1:10:1018895.
doi: 10.3389/fbioe.2022.1018895. eCollection 2022.

Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization

Zongshan Wang  1   2 , Hongwei Ding  1   2 , Jingjing Yang  1   2 , Peng Hou  3 , Gaurav Dhiman  4   5   6 , Jie Wang  7 , Zhijun Yang  1   2 , Aishan Li  8
Affiliations

Orthogonal pinhole-imaging-based learning salp swarm algorithm with self-adaptive structure for global optimization

Zongshan Wang et al. Front Bioeng Biotechnol. .

Abstract

Salp swarm algorithm (SSA) is a simple and effective bio-inspired algorithm that is gaining popularity in global optimization problems. In this paper, first, based on the pinhole imaging phenomenon and opposition-based learning mechanism, a new strategy called pinhole-imaging-based learning (PIBL) is proposed. Then, the PIBL strategy is combined with orthogonal experimental design (OED) to propose an OPIBL mechanism that helps the algorithm to jump out of the local optimum. Second, a novel effective adaptive conversion parameter method is designed to enhance the balance between exploration and exploitation ability. To validate the performance of OPLSSA, comparative experiments are conducted based on 23 widely used benchmark functions and 30 IEEE CEC2017 benchmark problems. Compared with some well-established algorithms, OPLSSA performs better in most of the benchmark problems.

Keywords: benchmark; equilibrium optimizer; exploration and exploitation; global optimization; metaheuristic algorithms; particle swarm optimization.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
The schematic diagram of PIBL on the leading salp.
FIGURE 2
FIGURE 2
Construct OPIBL leader.
FIGURE 3
FIGURE 3
The inertia weight ω curve.
FIGURE 4
FIGURE 4
The flow chart of OPLSSA.
FIGURE 5
FIGURE 5
Radar plot for consolidated ranks of 23 benchmarkproblems with the SSA variants.
FIGURE 6
FIGURE 6
Radar plot for consolidated ranks of 23 benchmark problems with OPLSSA and the Frontier algorithms.
FIGURE 7
FIGURE 7
Convergence curves of OPLSSA and other SSA-based algorithms on 15 representative benchmarks.
FIGURE 8
FIGURE 8
Convergence curves of OPLSSA and other Frontier algorithms on 15 representative benchmarks.
See this image and copyright information in PMC

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