An efficient salp swarm algorithm based on scale-free informed followers with self-adaption weight

Wang, Chao; Xu, Ren-qian; Ма, Лей; Zhao, Jie; Wang, Lu; Xie, Nenggang; Cheong, Kang Hao

doi:10.1007/s10489-022-03438-y

Cited by 17 publications

(3 citation statements)

References 67 publications

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“…In this subsection, to further investigate the efficiency of the proposed SSA variant, we compared SCQ-SSA with original slap swarm algorithm (SSA, [15]) and six other SSA variants, including Gaussian slap swarm algorithm (GSSA, [34]), Cauchy slap swarm algorithm (CSSA, [34]), Levy slap swarm algorithm (LSSA, [34]), Weight factor and adaptive mutation slap swarm algorithm (WASSA, [35]), Craziness and adaptive slap swarm algorithm (CASSA, [36]), Efficient salp swarm algorithm (ESSA, [37]). In this experiment, the number of dimensions is set to 30, the maximum number of iterations is set to 5000, the population size is 100, and each experiment is run 40 times.…”

Section: Comparison With the Ssa Variantsmentioning

confidence: 99%

“…The mathematical formulation of this problem is as follows: We analyze the structural weight design of tension/compression spring in this paragraph. Table 6 summarizes the comparison results between the proposed SSA variants and Velocity pausing particle swarm optimization (VPPSO, [40]), Elite archives-driven particle swarm optimization (EAPSO, [51]), Termite life cycle optimizer (TLCO, [52]), Sand cat swarm optimization (SCSO, [53]), Exploitation-boosted sine cosine algorithm (EBSCA, [54]), adaptive quadratic interpolation and rounding mechanism sine cosine algorithm (ARSCA, [55]), quantum particle swarm optimization with optimal guided Lévy flight and straight flight (LSFQPSO, [56]), adaptive cooperative foraging and dispersed foraging strategies harris hawks optimization (ADHHO, [57]), Parallel fish migration optimization with compact technology (PCFMO, [58]), efficient salp swarm algorithm (ESSA, [37]).…”

Section: Spring Design Problemmentioning

confidence: 99%

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A Novel Variant of the Salp Swarm Algorithm for Engineering Optimization

Jia¹,

Luo

Yin

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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There are many design problems need to be optimized in various fields of engineering, and most of them belong to the NP-hard problem. The meta-heuristic algorithm is one kind of optimization method and provides an effective way to solve the NP-hard problem. Salp swarm algorithm (SSA) is a nature-inspired algorithm that mimics and mathematically models the behavior of slap swarm in nature. However, similar to most of the meta-heuristic algorithms, the traditional SSA has some shortcomings, such as entrapment in local optima. In this paper, the three main strategies are adopted to strengthen the basic SSA, including chaos theory, sine-cosine mechanism and the principle of quantum computation. Therefore, the SSA variant is proposed in this research, namely SCQ-SSA. The representative benchmark functions are employed to test the performances of the algorithms. The SCQ-SSA are compared with the seven algorithms in high-dimensional functions (1000 dimensions), seven SSA variants and six advanced variants on benchmark functions, the experiment reveals that the SCQ-SSA enhances resulting precision and alleviates local optimal problems. Besides, the SCQ-SSA is applied to resolve three classical engineering problems: tubular column design problem, tension/compression spring design problem and pressure vessel design problem. The design results indicate that these engineering problems are optimized with high accuracy and superiority by the improved SSA. The source code is available in the URL: https://github.com/ye-zero/SCQSSA/tree/main/SCQ-SSA.

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Section: Comparison With the Ssa Variantsmentioning

confidence: 99%

Section: Spring Design Problemmentioning

confidence: 99%

A Novel Variant of the Salp Swarm Algorithm for Engineering Optimization

Jia¹,

Luo

Yin

et al. 2023

Journal of Artificial Intelligence and Soft Computing Research

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“…Particle Swarm Optimization (PSO) [ 47 ] is the most classic SI, inspired by the social behavior of birds and often used to solve various global optimization problems. Famous SI algorithms also include, but are not limited to, Ant Colony Optimization (ACO) [ 48 ], based on the collective behavior of ant colony, Moth–Flame Optimization (MFO) [ 49 , 50 , 51 ], the Grey Wolf Optimizer (GWO) [ 52 ] simulating the cooperative hunting behavior of gray wolves, the Whale Optimization Algorithm (WOA) [ 53 ], Harris Hawk Optimization (HHO) [ 54 ], the Black Widow Algorithm [ 55 , 56 ], the Seagull Optimization algorithm (SOA) [ 57 ], the Salp Swarm Algorithm (SSA) [ 58 , 59 ], the African Vultures Optimization Algorithm (AVOA) [ 60 ], the Dwarf Mongoose Optimization Algorithm (DMOA) [ 61 ], the Pelican Optimization Algorithm (POA) [ 62 ], Golden Jackal Optimization (GJO) [ 63 ], the Artificial Hummingbird Algorithm (AHA) [ 64 ], etc. Among them, AHA is a recently proposed bionic MA that is inspired by the intelligent foraging behaviors, special flight skills, and amazing memory function of hummingbirds.…”

Section: Introductionmentioning

confidence: 99%

PSO-Incorporated Hybrid Artificial Hummingbird Algorithm with Elite Opposition-Based Learning and Cauchy Mutation: A Case Study of Shape Optimization for CSGC–Ball Curves

Chen,

2023

Biomimetics

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With the rapid development of the geometric modeling industry and computer technology, the design and shape optimization of complex curve shapes have now become a very important research topic in CAGD. In this paper, the Hybrid Artificial Hummingbird Algorithm (HAHA) is used to optimize complex composite shape-adjustable generalized cubic Ball (CSGC–Ball, for short) curves. Firstly, the Artificial Hummingbird algorithm (AHA), as a newly proposed meta-heuristic algorithm, has the advantages of simple structure and easy implementation and can quickly find the global optimal solution. However, there are still limitations, such as low convergence accuracy and the tendency to fall into local optimization. Therefore, this paper proposes the HAHA based on the original AHA, combined with the elite opposition-based learning strategy, PSO, and Cauchy mutation, to increase the population diversity of the original algorithm, avoid falling into local optimization, and thus improve the accuracy and rate of convergence of the original AHA. Twenty-five benchmark test functions and the CEC 2022 test suite are used to evaluate the overall performance of HAHA, and the experimental results are statistically analyzed using Friedman and Wilkerson rank sum tests. The experimental results show that, compared with other advanced algorithms, HAHA has good competitiveness and practicality. Secondly, in order to better realize the modeling of complex curves in engineering, the CSGC–Ball curves with global and local shape parameters are constructed based on SGC–Ball basis functions. By changing the shape parameters, the whole or local shape of the curves can be adjusted more flexibly. Finally, in order to make the constructed curve have a more ideal shape, the CSGC–Ball curve-shape optimization model is established based on the minimum curve energy value, and the proposed HAHA is used to solve the established shape optimization model. Two representative numerical examples comprehensively verify the effectiveness and superiority of HAHA in solving CSGC–Ball curve-shape optimization problems.

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