Performance of chaotic mapping multi-objective optimization algorithms

Abstract

Multi-objective optimization is defined as the process of producing suitable solutions to problems with multiple objectives. The randomly generated string of numbers is of great importance in achieving solutions close to the global optimum in intuitive multi- objective optimization. Collecting the randomly generated string of numbers in a certain area increases the risk of moving away from the global optimum. Chaotic maps are used to reduce this risk it is not periodic as the variety of numbers produced in chaotic maps is high. For this reason, chaotic maps are used in the random number generation part of optimization algorithms. Chaos-based algorithms have become an important field of study because they are flexible and can escape from local minimums. In this study, the effects of chaotic maps on the new and successful Multi-objective Gold Sine Algorithm (MOGoldSA) were compared with the Multi-Objectıve Ant Lion Optimization (MOALO) algorithm.