Multi-Hive Artificial Bee Colony Algorithm for Constrained Multi-Objective Optimization

This paper presents a general cooperative coevolution model inspired by the concept and main ideas of the coevolution of symbiotic species in natural ecosystems. A novel approach called "multi-hive artificial bee colony" for constrained multi-objective optimization (MHABC-CMO) is proposed based on this model. A novel information transfer strategy among multiple swarms and division operator are proposed in MHABC-CMO to tie it closer to natural evolution, as well as improve the robustness of the algorithm. Simulation experiment of MHABC-CMO on a set of benchmark test functions are compared with other nature inspired techniques which includes multi-objective artificial bee colony (MOABC), nondominated sorting genetic algorithm II (NSGA II) and multi-objective particle swarm optimization (MOPSO). The numerical results demonstrate MHABC-CMO approach is a powerful search and optimization technique for constrained multi-objective optimization.