Adaptive Truncation technique for Constrained Multi-Objective Optimization

Abstract

The performance of evolutionary algorithms can be seriously weakened when constraints limit the feasible region of the search space. In this paper we present a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique, ε-truncation technique keeps an effective balance between feasible solutions and infeasible solutions by permitting some excellent infeasible solutions with good objective value and low constraint violation to take part in the evolution, so diversity is improved, and convergence is also coordinated. Next, an exponential variation is introduced after differential mutation and crossover to boost the local exploitation ability. At last, the improved crowding density method only selects some Pareto solutions and near solutions to join in calculation, thus it can evaluate the distribution more accurately. The comparative results with other state-of-the-art algorithms show that ε-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent.

Self-Adaptive Constrained Multi-Objective Differential Evolution Algorithm Based on the State–Action–Reward–State–Action Method

“…When a search operator hits a difficult patch in the search space, the scheme "reacts" to that by potentially calling upon a different search operator. Zhang et al [29] proposed a constrained multi-objective optimization algorithm based on adaptive ε-truncation (ε-T-CMOA) to further improve the distribution and convergence of the obtained solutions. In [30], an adaptive repair approach was proposed to improve the efficiency of constraint handling in non-dominance.…”