Modified Predator-prey (mpp) Algorithm for Constrained Multi-objective Optimization

In this work, an evolutionary multi-objective optimization algorithm based on the dynamics of predator-prey interactions existing in nature is presented. This algorithm is comprised of a relatively small number of predators and a much larger number of prey, randomly placed on a two dimensional lattice with connected ends. The predators are partially or completely biased towards one or more objectives, based on which each predator kills the weakest prey in its neighborhood. A stronger prey created through evolution replaces this prey. The prey remain stationary, while the predators move around in the lattice during the course of generations. Modifications of the basic predator-prey algorithm have been implemented in this study regarding the selection procedure, apparent movement of the predators, mutation strategy, dynamics of the Pareto convergence and a capability of handling equality and inequality constraints. The final modified algorithm was tested on standard constrained and unconstrained multiobjective optimization problems.