Association for Structural and Multidisciplinary Optimization in the UK (ASMO-UK)

In this work, an evolutionary multi-objective optimization algorithm (EMO) based on the dynamics of predator-prey interactions existing in nature is presented. This algorithm contains “prey”, which represent members of the population/sample space and are randomly placed on a two dimensional lattice with connected ends. “Predators” are comparatively fewer in number than “prey” and are placed in the cell centers of the same 2D lattice. The predators are partially or completely biased towards one or more objectives. Based on this quantitative nature, each predator kills the weakest prey in its locality and replaces it with better prey. The prey remains stationary, while the predators move around in the lattice during the course of generations improving the quality of the prey relating to the objectives (objective functions to be maximized or minimized) of the problem. The basic concept of the predator-prey algorithm was suggested by Dr. Hans Paul Schwefel and reported by Laumann et al. in 1998. In the present paper, significant modifications of the basic algorithm regarding the selection procedure, apparent movement of the predators, and the general dynamics of the Pareto convergence have been proposed. The final modified form has been applied to standard two-objective optimization problems.