Tri-Goal Evolution Framework for Constrained Many-Objective Optimization

Zhou, Yue; Zhu, Min; Wang, Jiahai; Zhang, Zizhen; Xiang, Yi; Zhang, Jun

doi:10.1109/tsmc.2018.2858843

Cited by 39 publications

(29 citation statements)

References 51 publications

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“…In order to prove the effectiveness of MOABCAG, thirteen benchmark test problems have been used for experimentation which are taken from two well-known test suites such as DTLZ [36] and WFG [37]. In our experiments, we compare MOABCAG with five well-established algorithms such as DGEA [38], LMOCSO [39], MaOEAIGD [21], MaOEAIT [40] and TiGE2 [41] . The experiments were conducted on a PC with a 1.8 GHz Intel Core i5-10510U CPU and Windows 10 64-bit operating system.…”

Section: Experimentation and Resultsmentioning

confidence: 99%

A Many-Objective Artificial Bee Colony Algorithm Based on Adaptive Grid

Zhang

2021

IEEE Access

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Many-objective optimization problems are widely applied and complex to solve. For most many-objective evolutionary algorithms, maintaining the balance of solution convergence and diversity is a challenging problem. Considering the convergence and diversity at the same time, a many-objective artificial bee colony algorithm based on adaptive grid(MOABCAG) is proposed. By searching for the ideal target point in the grid, the solution selection pressure is enhanced, the solution is positioned under the designed grid, and the grid is adaptively divided to improve the convergence and diversity of the solution. The algorithm improves the position sharing mechanism of the leader and follower bees in the artificial bee colony algorithm, and it sets the variable neighborhood search method of the follower bee to improve the precision of the solution vectors. MOABCAG is compared with five well-known evolutionary algorithms on thirteen benchmark test functions. The results show that the proposed MOABCAG algorithm obtains better performance than other related state-of-the-art algorithms in solving such many-objective optimization problems.

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Section: Experimentation and Resultsmentioning

confidence: 99%

A Many-Objective Artificial Bee Colony Algorithm Based on Adaptive Grid

Zhang

2021

IEEE Access

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“…In this section, the performance of CMME is extensively evaluated by comparing with nine related methods on the C-DTLZ [24], DC-DTLZ [10], DAS-CMOP [4], MW [17] test functions and three real-world CMOPs [49]. 2) Algorithms for Comparison: To indicate the superiority of CMME, three CMOEAs (i.e., NSGA-II-ToR [25], ToP [34], and CCMO [26],) and six CMaOEAs (i.e., NSGA-III [24], I-DBEA [40], PPS [31], C-TAEA [10], TiGE-2 [45], and DCNSGA-III [28]) were selected for comparison. All of these methods were implemented on the PlatEMO [50].…”

Section: Experimental Results and Analysismentioning

confidence: 99%

“…In addition, CMME obtained significantly better results on the majority of the test cases than NSGA-II-ToR(47), ToP(45), CCMO(30), NSGA-III (28), I-DEBA(47), PPS(35), C-TAEA (30), TiGE-2(42), and DCNSGA-III(27).• Again, CMME had the best IGD ranking among the 10 algorithms.• Finally, CMME significantly outperformed the other nine algorithms based on the Wilcoxon test shown in TableV. For DC3-DTLZ, the current E max does not seem to be enough for most CMaOEAs to converge to the CPF; therefore, the results of HV and IGD contain many NaN and null values.…”

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confidence: 99%

A Constrained Many-objective Optimization Evolutionary Algorithm with Enhanced Mating and Environmental Selections

Ming¹

2021

Preprint

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<div>Unlike the considerable research on solving many objective optimization problems with evolutionary algorithms, there has been much less research on constrained many-objective optimization problems (CMaOPs). Generally, to effectively solve CMaOPs, an algorithm needs to balance feasibility, convergence, and diversity simultaneously. It is essential for handling CMaOPs yet most of the existing research encounters difficulties. This paper proposes a novel constrained many-objective optimization evolutionary algorithm with enhanced mating and environmental selections, namely CMME. The main features are: i) two ranking strategies are proposed and applied in the mating and environmental selections to enrich feasibility and convergence; ii) an individual density estimation is designed, and crowding distance is integrated to promote diversity; and iii) the ?-dominance is used to strengthen the selection pressure on both the convergence and diversity. The synergy of these components can achieve the goal of balancing feasibility, convergence, and diversity for solving CMaOPs. The proposed CMME algorithm is evaluated on 10 CMaOPs with different features and a variable number of objective functions. Experimental results on three benchmark CMOPs and three real-world applications demonstrate that CMME shows superiority or competitiveness over nine related algorithms.</div>

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“…In [19], a constrained non-dominated rank is defined by integrating the original non-dominated rank and a constraint rank. In [20], a tri-goal evolutionary framework is proposed to consider convergence, diversity, and feasibility.…”

Section: A Existing Cmoeasmentioning

confidence: 99%

Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization

Tian

Zhang

et al. 2022

IEEE Trans. Cybern.

100

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Both objective optimization and constraint satisfaction are crucial for solving constrained multi-objective optimization problems, but existing evolutionary algorithms encounter difficulties in striking a good balance between them when tackling complex feasible regions. To address this issue, this paper proposes a two-stage evolutionary algorithm, which adjusts the fitness evaluation strategies during the evolutionary process to adaptively balance objective optimization and constraint satisfaction. The proposed algorithm can switch between the two stages according to the status of the current population, enabling the population to cross the infeasible region and reach the feasible regions in one stage, and to spread along the feasible boundaries in the other stage. Experimental studies on four benchmark suites and three real-world applications demonstrate the superiority of the proposed algorithm over the state-of-theart algorithms, especially on problems with complex feasible regions.

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Tri-Goal Evolution Framework for Constrained Many-Objective Optimization

Cited by 39 publications

References 51 publications

A Many-Objective Artificial Bee Colony Algorithm Based on Adaptive Grid

A Many-Objective Artificial Bee Colony Algorithm Based on Adaptive Grid

A Constrained Many-objective Optimization Evolutionary Algorithm with Enhanced Mating and Environmental Selections

Balancing Objective Optimization and Constraint Satisfaction in Constrained Evolutionary Multiobjective Optimization

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