Use of a self-adaptive penalty approach for engineering optimization problems
Computers in Industry
Journal of Global Optimization
An effective co-evolutionary particle swarm optimization for constrained engineering design problems
Engineering Applications of Artificial Intelligence
Evolutionary algorithms for constrained parameter optimization problems
Evolutionary Computation
Expert Systems with Applications: An International Journal
Fundamenta Informaticae - Swarm Intelligence
Society and civilization: An optimization algorithm based on the simulation of social behavior
IEEE Transactions on Evolutionary Computation
Hi-index | 12.05 |
Over the years, several metaheuristics have been developed to solve hard constrained and unconstrained optimization problems. In general, a metaheuristic is proposed and following researches are made to improve the original algorithm. In this paper, we evaluate a not so new metaheuristic called differential evolution (DE) to solve constrained engineering design problems and compare the results with some recent metaheuristics. Results show that the classical DE with a very simple penalty function to handle constraints is still very competitive in the tested problems.