A spatial predator-prey approach to multi-objective optimization: A preliminary study

Abstract

Abstract. This paper presents a novel evolutionary approach of approximating the shape of the Pareto-optimal set of multi-objective optimization problems. The evolutionary algorithm (EA) uses the predator-prey model from ecology. The prey are the usual individuals of an EA that represent possible solutions to the optimization task. They are placed at vertices of a graph, remain stationary, reproduce, and are chased by predators that traverse the graph. The predators chase the prey only within its current neighborhood and according to one of the optimization criteria. Because there are several predators with different selection criteria, those prey individuals, which perform best with respect to all objectives, are able to produce more descendants than inferior ones. As soon as a vertex for the prey becomes free, it is refilled by descendants from alive parents in the usual way of EA, i.e., by inheriting slightly altered attributes. After a while, the prey concentrate at Pareto-optimal positions. The main objective of this preliminary study is the answer to the question whether the predator-prey approach to multi-objective optimization works at all. The performance of this evolutionary algorithm is examined under several step-size adaptation rules.

Approach for solving multimodal problems using Genetic Algorithms with Grouped into Species optimized with Predator-Prey

“…The initial diagram regarding the operation of the classical algorithm of the predator-prey approach had already taken this aspect into account. Although modifications and optimizations have been made since its inception, the diagram we have broadly followed is classical [3], as detailed below.…”

“…To avoid the created drawbacks, the concept of predatorprey [3] is introduced in the system applying it to species in the area. The technique is firstly designed to distribute individuals behaving as prey randomly in an area and then do the same with some individuals called predators, which, according to some rules, deal with deleting some of the prey in the neighborhood.…”

Multi-criteria scheduling: an agent-based approach for expert knowledge integration

“…In contrast to the monolithic structure of the previously discussed approaches, Schwefel et al [17] proposed an agentbased approach motivated from natural interaction of predators and prey to specifically solve multi-criteria optimization problems. We adapt this algorithmic idea and transfer its application with several modifications to scheduling problems.…”

Techniques for Maintaining Population Diversity in Classical and Agent-Based Multi-objective Evolutionary Algorithms

“…Laumanns, Rudolph and Schwefel proposed co-evolutionary algorithm with spatial graph-like structure and predator-prey model for multi-objective optimization [13]. Deb introduced modified algorithm in which predators eliminated preys not only on the basis of one criteria but on the basis of the weighted sum of all criteria [5].…”