An Elastic Collision Seeker Optimization Algorithm for Optimization Constrained Engineering Problems

Shaomi Duan,Huilong Luo,Haipeng Liu,Aditya Rio Prabowo

doi:10.1155/2022/1344667

Shaomi Duan, Huilong Luo + Show 2 more

Open Access

https://doi.org/10.1155/2022/1344667

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Abstract

To improve the seeker optimization algorithm (SOA), an elastic collision seeker optimization algorithm (ECSOA) was proposed. The ECSOA evolves some individuals in three situations: completely elastic collision, completely inelastic collision, and non-completely elastic collision. These strategies enhance the individuals’ diversity and avert falling into the local optimum. The ECSOA is compared with the particle swarm optimization (PSO), the simulated annealing and genetic algorithm (SA_GA), the gravitational search algorithm (GSA), the sine cosine algorithm (SCA), the multiverse optimizer (MVO), and the seeker optimization algorithm (SOA); then, fifteen benchmark functions, four PID control parameter models, and six constrained engineering optimization problems were selected for the experiment. According to the experimental results, the ECSOA can be used in the benchmark functions, the PID control parameter optimization, and the optimization constrained engineering problems. The optimization ability and robustness of ECSOA are better.

Highlights

The heuristic algorithm has received a lot of attention
The SOA is improved by seven different methods: the parameter changing SOA (PCSOA), the parameter adaptive Gaussian transform SOA (PAGTSOA), the SOA based on Levy variation (LVSOA), the SOA based on refraction reverse learning mechanism (RRLSOA), the SOA based on mutually beneficial factor strategy (MBFSOA), the SOA based on Cauchy variation (CVSOA), and the elastic collision seeker optimization algorithm (ECSOA)
The SOA based on parameter adaptive Gaussian transform (PAGTSOA), the SOA based on Levy variation (LVSOA), the SOA based on refraction reverse learning mechanism (RRLOOA), the SOA based on mutual benefit factor strategy (MBFSOA), the SOA based on Cauchy variation (CVSOA), and the SOA based on elastic collision (ECSOA)

Summary

Introduction

The heuristic algorithm has received a lot of attention. Such algorithms create random methods for many optimization problems. E ECSOA is compared to seven improved SOAs, such as the changing algorithm parameters, the adaptive transformation of empirical value parameters, the Levy motion of some individuals, the reverse learning, the addition of mutual benefit factor, and the Cauchy mutation. (2) e elastic collision strategies, the completely elastic collision, the completely inelastic collision, and the non-complete elastic collision, can improve the diversity of individuals, enhance local search, and avert premature convergence. Where i represents the ith individual, j represents the individual dimension, ψ represents a random number in (0,1), xgbest represents the j-dimensional component of the current optimal position of the entire population, C is the mutual benefit factor, R is the benefit parameter, and 1 or 2 is randomly selected.

Experimental Results

Result

Conclusion

Three-Bar Truss Design Problem