A New Evolutionary Strategy for Pareto Multi-Objective Optimization

Many different evolutionary algorithms are applicable to solve a Pareto multiobjective optimization problem, to find a set of optimal solutions that collectively form a Pareto front. These algorithms generally do not provide any guidance for selecting best-compromise solutions to the problem. Such selection can be a daunting task when, as is often the case, a large number of solutions populate the Pareto front in a complex objective space. To address this issue, the present paper proposes a genetic algorithm for which the computational process to establish the Pareto front is driven by the fitness of an evolving best-compromise solution that uniquely represents a mutually agreeable trade-off between all competing objectives for the problem. The proposed evolutionary strategy is illustrated for a variety of standard test problems having from two to five objective criteria constrained such that the Pareto front is either connected or disconnected. The paper concludes with an assessment of the merits of the proposed strategy to use a unique Paretocompromise solution to drive the fitness calculations of the evolutionary process.