A hybrid engineering algorithm of the seeker algorithm and particle swarm optimization

Abstract

Abstract A newly hybrid algorithm is proposed based on the combination of seeker optimization algorithm and particle swarm optimization. The hybrid algorithm is based on a double population evolution strategy, and the populations of individuals are evolved from the seeker optimization algorithm and the particle swarm optimization separately. The populations of individuals employ an information sharing mechanism to implement coevolution. The hybrid algorithm enhances the individuals’ diversity and averts fall into the local optimum. The hybrid algorithm is compared with particle swarm optimization, the simulated annealing and genetic algorithm, the dragonfly algorithm, the brain storming algorithm, the gravitational search algorithm, the sine cosine algorithm, the salp swarm algorithm, the multi-verse optimizer, and the seeker optimization algorithm, then 15 benchmark functions, five proportional integral differential control parameters models, and six constrained engineering optimization problems are selected for optimization experiment. According to the experimental results, the hybrid algorithm can be used in the benchmark functions, the proportional integral differential control parameters optimization, and in the optimization constrained engineering problems. The optimization ability and robustness of the hybrid algorithm are better.