Abstract
When solving constrained multi-objective optimization problems, an important issue is how to balance convergence, diversity and feasibility simultaneously. To address this issue, this paper proposes a parameter-free constraint handling technique, two-archive evolutionary algorithm, for constrained multi-objective optimization. It maintains two co-evolving populations simultaneously: one, denoted as convergence archive, is the driving force to push the population toward the Pareto front; the other one, denoted as diversity archive, mainly tends to maintain the population diversity. In particular, to complement the behavior of the convergence archive and provide as much diversified information as possible, the diversity archive aims at exploring areas under-exploited by the convergence archive including the infeasible regions. To leverage the complementary effects of both archives, we develop a restricted mating selection mechanism that adaptively chooses appropriate mating parents from them according to their evolution status. Comprehensive experiments on a series of benchmark problems and a real-world case study fully demonstrate the competitiveness of our proposed algorithm, comparing to five state-of-the-art constrained evolutionary multi-objective optimizers.
Highlights
T HE CONSTRAINED multiobjective optimization problem (CMOP) considered in this paper is defined as minimize F(x) = (f1(x), . . . , fm(x))T subject to gj(x) ≥ aj, j = 1, . . . , q hj(x) = bj, j = q + 1, . . . , x∈ (1)where x = (x1, . . . , xn)T is a candidate solution, and = [xiL, xiU]n ⊆ Rn defines the search space
convergence-oriented archive (CA), as the main force, is mainly responsible for driving the population toward the feasible region and approximating the Pareto front (PF); diversity-oriented archive (DA), as a complement, is mainly used to explore the areas under-exploited by the CA
We have suggested a parameter-free constraint handling technique, C-TAEA, for constrained multiobjective optimization
Summary
Convergence, diversity, and feasibility are three basic issues for CMOPs. Most, if not all, current constraint handling techniques at first tend to push a population toward the feasible region as much as possible, before considering the balance between convergence and diversity within the feasible region. If not all, current constraint handling techniques at first tend to push a population toward the feasible region as much as possible, before considering the balance between convergence and diversity within the feasible region This might lead to the population being stuck at some locally optimal or locally feasible regions, especially when the feasible regions are narrow and/or disparately distributed in the search space. [6]–[8] developed several twoarchive EMO algorithms that use two “conceptually” complementary populations to strike the balance between convergence and diversity of the evolutionary process.
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