MOEA stands for "Multi-Objective Evolutionary Algorithm." It is a computational technique used in the field of evolutionary computation to solve complex optimization problems that involve multiple conflicting objectives. MOEA is a metaheuristic algorithm that mimics the process of natural evolution to search for optimal solutions.
The main characteristic of MOEA is its ability to handle multiple objectives simultaneously. In traditional optimization problems, a single objective is defined, and the algorithm aims to find the best solution for that objective. However, in real-world scenarios, multiple conflicting objectives often need to be considered simultaneously. MOEA tackles this challenge by employing a population-based approach, maintaining a set of solutions called the Pareto front, where no solution is better than another in every objective.
The algorithm employs several genetic operators, such as selection, crossover, and mutation, to evolve the population and gradually improve the quality of solutions on the Pareto front. MOEA evaluates the fitness of each solution based on its performance in multiple objectives, using metrics like dominance, diversity, and density. By iteratively applying these operators, MOEA explores the solution space, gradually converging towards the optimal trade-off solutions for the multiple objectives.
MOEA has found application in various fields, including engineering design, finance, logistics, and resource allocation. Its versatility and ability to handle multiple objectives make it an effective tool for decision-making and solving complex optimization problems, where single-objective algorithms may not be suitable.
