Evolutionary algorithm

Algorithm

Philosophy

Archaeology

Computation

Constraint (computer-aided design)

Computer science

Mathematical optimization

Evolutionary computation

Regression analysis

Suite

Mathematics

Test case

Epistemology

Artificial intelligence

Geometry

Strengths and weaknesses

Test suite

Machine learning

Multi-objective optimization

History

IEEE Transactions on Evolutionary Computation

For solving constrained multiobjective optimization problems (CMOPs), many algorithms have been proposed in the evolutionary computation research community for the past two decades. Generally, the effectiveness of an algorithm for CMOPs is evaluated by artificial test problems. However, after a brief review of current artificial test problems, we have found that they are not well-designed and fail to reflect the characteristics of real-world applications (e.g., small feasibility ratio). Thus, in this paper, we first propose a new constraint construction method to facilitate the systematic design of test problems. Then, on the basis of this method, we design a new test suite consisting of 14 instances, which covers diverse characteristics extracted from real-world CMOPs and can be divided into four types. Considering that the comprehensive performance comparisons among the constraint-handling techniques (CHTs) remain scarce, we choose several representative CHTs and compare their performance on our test suite. The performance comparisons identify the strengths and weaknesses of different CHTs on different types of CMOPs and provide guidelines on how to select/design a CHT in a specific scenario.

Evolutionary Constrained Multiobjective Optimization: Test Suite Construction and Performance Comparisons