An advanced hybrid meta-heuristic algorithm for solving small- and large-scale engineering design optimization problems

Abstract An advanced hybrid algorithm ( h aDEPSO) is proposed in this paper for small- and large-scale engineering design optimization problems. Suggested advanced, differential evolution (aDE) particle swarm (aPSO) integrated with aDEPSO. In aDE a novel, mutation, crossover selection strategy introduced, to avoid premature convergence. And aPSO consists of novel gradually varying parameters, escape stagnation. So, convergence characteristic provides different approximation the solution space. Thus, aDEPSO achieve better solutions due integrating merits aPSO. Also individual population merged other pre-defined manner, balance between global local search capability. The performance its component are validated on 23 unconstrained benchmark functions, then solved five small (structural engineering) one large (economic load dispatch)-scale Outcome analyses confirm superiority algorithms over many state-of-the-art algorithms.