HyCAPS: A Settings-Free Optimization Heuristics Integrating Evolutionary Computation and Swarm Intelligence

Evolutionary Computation (EC) and Swarm Intelligence (SI) include some of the most successful meta-heuristics for global optimization. In practical applications, selecting the most suitable EC or SI meta-heuristics as well as the proper setting of its hyper-parameters is usually expensive in terms of computational resources and might be troublesome for novice users. To overcome these downsides, we propose a novel global optimization method, called “Hybrid CMA-PSO” (HyCAPS), which does not demand any complicated procedure for the identification of the hyper-parameters values as it exploits two settings-free algorithms: covariance matrix adaptation evolution strategy (CMA-ES) from EC, and Fuzzy Self-Tuning Particle Swarm Optimization (FST-PSO) from SI. HyCAPS is structured as a two-islands algorithm that evolves one population running CMA-ES and another running FST-PSO, which cooperate by exchanging the fittest individuals at a given frequency. We show that HyCAPS outperforms four optimization methods – FST-PSO, jSO, NL-SHADE-RSP, NL-SHADE-LBC – and competes with CMA-ES and SHADE-ILS on many high-dimensional, continuous, and bounded optimization problems taken from the IEEE CEC’05 and CEC’17 benchmark suites. Our results also highlight that the cooperation between the islands limits the risk of getting stuck in local optima and enhances the search space exploration. We believe that HyCAPS will represent an advantageous meta-heuristics in real-case scenarios where no information about the optimization problem is generally available.