Hybrid ICA–PSO algorithm for continuous optimization

• A novel hybrid metaheuristic fro continuous problems is proposed.
• The algorithm combines particle swarm optimization (PSO) and imperialist competitive algorithms (ICA).
• Algorithm performance is tested on several single and multi-objective problems.
• Results show that the algorithm outperforms NSGA-II and SPEA2.

This paper proposes a new hybrid ICA–PSO algorithm designed to solve mono-objective and multi-objective problems. This continuous optimization algorithm combines an imperialist competitive algorithm (ICA) and a particle swarm optimization (PSO) to improve the exploration ability of ICA. Moreover, ICA–PSO employs the crowding distance to maintain diversity in the Pareto front found by the algorithm as well as a crossover operator to improve the quality of the solutions in the memory of each individual. We test the performance of the proposed algorithm on mono-objective and multi-objective benchmark functions and three engineering design problems, thus showing its ability to optimize different types of optimization problems. Numerical results indicate that our approach outperforms several recently published algorithms, such as NSGA-II and SPEA2.