Cylindrical Constraint Evolutionary Algorithm for Multiobjective Optimization

This paper introduces a new iterative evolutionary algorithm, which is able to provide an evenly distributed set of solutions in multiobjective context. The method is different from the other evolutionary algorithms in two perspectives. First, instead of density information incorporated to find a diverse set of solutions, a hypercylinder is introduced as a new constraint to the problem. Searching for the solution within this hypercylinder guarantees the evenly generated solutions at the end of the optimization process. Second, a fitness function is constructed to handle the problem constraints and meanwhile minimize the distance of the solution to the true optimum frontier. In addition, the method is developed in such a way that it can be easily implemented in searching the preferable region of the search space. The algorithm behaviour is tested on different test cases and the results are compared in both convergence and diversity to those of other well known approaches to demonstrate the efficacy of the proposed method.