Novelty-based multi-objectivization for unbounded search space optimization

Abstract

Unlike the conventional swarm or evolutionary optimizations that are generally assumed the “pre-defined” bounded search space, this paper addresses the optimization for the “unbounded” search space. For this purpose, this paper proposes novelty-based multi-objectivization with local and rough area search (NM-LRS), which adds the novelty criterion in the given optimization criteria to roughly search the unbounded search space for obtaining the “potential area” where the optimal solution is most likely located and then searches the “potential area” to find the optimal solution by a local area search. To investigate the effectiveness of the proposed methods, the experiment compares the proposed methods with the conventional optimization methods for the unbounded multi-modal optimization and has revealed the following implications: (i) the peak ratio (i.e., the ratio of the founded peaks of the multi-modal function) of NM-LRS is higher than that of the conventional methods; and (ii) NM-LRS is robust for the location of the initial search area in the most functions.