Adaptive Truncation technique for Constrained Multi-Objective Optimization

• Lei Zhang,
• Xiaojun Bi,
• Yanjiao Wang,

Vol. 13, No. 11, November 30, 2019
10.3837/tiis.2019.11.012, Download Paper (Free):

• evolutionary computing
• constrained multi-objective optimization
• constraint handling
• diversity maintenance
• convergence

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 琯-truncation (琯-T-CMOA) to further improve distribution and convergence of the obtained solutions. First of all, as a novel constraint handling technique, 琯-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 琯-T-CMOA is more diverse than the other algorithms and it gains better in terms of convergence in some extent.

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