Cohort Intelligence-Based Multi-Objective Optimizer

In multi-objective optimization problems, circumstances frequently arise where an improvement in one of the objectives may worsen one or more of the other objectives. Decision-makers often face challenges in balancing multiple objectives since there will rarely be a solution wherein all the objectives are optimal. Thus, there is a need to present a set of trade-offs that serves as a viable answer to such challenges. This is where multi-objective optimization algorithms come in, providing a set of choices to balance these objectives, from which the decision-maker can choose the best trade-off for the problem at hand. The decision-makers may utilize domain expertise to arrive at an acceptable decision. In this chapter, we propose a novel approach built on the foundations of the Cohort Intelligence (CI) framework, called the cohort intelligence-based multi-objective optimizer (CIMO) algorithm. This algorithm can search for solutions to multi-objective optimization problems. The solutions obtained demonstrate its efficiency in searching for non-dominated solutions. A variety of problems were solved to check the viability of the algorithm to tackle real-world optimization challenges, and the results demonstrated the ability of the algorithm to provide the much-needed diversity in the solution set.