Self-tuning Co-Operation of Biology-Inspired and Evolutionary Algorithms for Real-World Single Objective Constrained optimization

Solving single objective constrained real-parameter optimization problems via population-based algorithms has attracted much attention. In this paper, a new self-tuning meta-heuristic approach called Fuzzy Controlled Cooperative Heterogeneous Algorithm (FCHA), which was proposed for constrained optimization, is introduced. The developed approach combines competition and cooperation between biology-inspired and evolutionary algorithms, regulated by fuzzy controller. It should be noted, that the epsilon-constrained method is utilized to handle the constraints for the solved optimization problems. The performance of the proposed FCHA algorithm is evaluated on 57 real-world constrained problems submitted for CEC 2020 special session. Its workability and usefulness are demonstrated; also ways of algorithm improvement are discussed.