An Elastic Collision Seeker Optimization Algorithm for Optimization Constrained Engineering Problems

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.