A hybrid algorithm based on Differential Evolution, Particle Swarm Optimization and Harmony Search algorithms

Evolutionary optimization algorithms and their hybrid forms have become popular for solving multimodal complex problems which are very difficult to solve by traditional methods in the recent years. In the literature, many hybrid algorithms are proposed in order to achieve a better performance than the well-known evolutionary optimization methods being used alone by combining their features for balancing the exploration and exploitation goals of the optimization algorithms. This paper proposes a novel hybrid algorithm composed of Differential Evolution algorithm, Particle Swarm Optimization algorithm and Harmony Search algorithm which is called HDPH. The proposed algorithm is compared with these three algorithms on the basis of solution quality and robustness. Numerical results based on several well-studied benchmark functions have shown that HDPH has a good solution quality with high robustness. Also, in HDPH all parameters are randomized which prevents the disadvantage of selecting all possible combination of parameter values in the selected ranges and of finding the best value set by parameter tuning.