Abstract
This article comes up with a complex-valued encoding multichain seeker optimization algorithm (CMSOA) for the engineering optimization problems. The complex-valued encoding strategy and the multichain strategy are leaded in the seeker optimization algorithm (SOA). These strategies enhance the individuals’ diversity, enhance the local search, avert falling into the local optimum, and are the influential global optimization strategies. This article chooses fifteen benchmark functions, four proportional integral derivative (PID) control parameter models, and six constrained engineering problems to test. According to the experimental results, the CMSOA can be used in the benchmark functions, in the PID control parameter optimization, and in the optimization of constrained engineering problems. Compared to the particle swarm optimization (PSO), simulated annealing based on genetic algorithm (SA_GA), gravitational search algorithm (GSA), sine cosine algorithm (SCA), multiverse optimizer (MVO), and seeker optimization algorithm (SOA), the optimization ability and robustness of the CMSOA are better than those of others algorithms.
Highlights
The heuristic algorithm has received a lot of attention
We propose the complexvalued encoding multichain seeker optimization algorithm (CMSOA). e multichain strategy includes the complexvalued multichain and the stochastic complex multichain strategy. e complex-valued encoding multichain seeker optimization algorithm (CMSOA) has been tested on fifteen benchmark functions, four proportional integral derivative (PID) control parameter optimizations, and six engineering optimizations taken from the literature
The SOA is improved by six different methods: the parameter changing SOA (PCSOA), the parameter adaptive Gaussian transform SOA (PAGTSOA), the SOA based on the Chebyshev chaos of order three (CCSOA), the SOA based on real coding double-link (DSOA), the SOA based on complex-valued encoding (CSOA), and the complex-valued encoding multichain seeker optimization algorithm (CMSOA)
Summary
The heuristic algorithm has received a lot of attention. Such algorithms create random methods for many optimization problems. Complex number coding and a multichain strategy are used to enhance the global optimization and the local search. E complex-valued encoding and the multichain methods enhance the diversity of the individuals and avert premature convergence. (2) With the complex-coded multichain strategy, in complex-valued coding, the real part, imaginary part, and real number are used as parallel individual variables to solve the objective function problem. According to the initial solution generation rule of the complex number coding, the real part, the imaginary part, and the real number are randomly generated as the parallel individual variables to solve the objective function. The meaning of the multichain strategy is that a single individual variable in the original SOA is converted into six parallel individual parameters when the CMSOA optimizes a problem.
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