Fitness and Collaborative Information-Driven Differential Evolution Algorithm for Bound-Constrained and Real-World Application Problems

The effectiveness of differential evolution (DE) is significantly impacted by the selection of the mutation operator and the setup of control parameters. However, their unique selection might not ensure the optimized search procedure that enables the algorithm to explore for a global optimal solution for the given optimization problem. Further, with this selection approach, the algorithm might suffer from the issues of getting stuck at local optima and premature convergence. To address these challenges, this paper proposes a new framework of the DE called fitness and collaborative information-driven DE (COLDE). In the COLDE, a novel mutation operator is proposed to strengthen the collaboration among elite and non-elite candidate solutions so that more promising offspring vectors can be generated. The scale factor parameters are adjusted according to the evolutionary state of candidate solutions engaged in the mutation operator, while the crossover operator is tuned based on the success rates of crossover parameters determined in the past evolutionary stage. Moreover, the population size is also reduced over the generations to discard the unfavorable candidate solutions. The validation of the proposed COLDE is conducted on the standard set of benchmark problems provided by the IEEE CEC2017 of real-parameter single-objective problems and eight real-world engineering optimization problems. A comparison of COLDE with other evolutionary algorithms using diverse performance metrics verifies its promising and competitive search efficiency against the compared algorithms.