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.
