A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy

Liu, Yiping; Gong, Dunwei; Sun, Jing; Jin, Yaochu

doi:10.1109/tcyb.2016.2638902

Cited by 245 publications

(77 citation statements)

References 60 publications

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“…is a metric for measuring the convergence degree of x, which has been widely used in many MOEAs [15], [33]- [35], θ xy denotes the acute angle between the objective values of the two candidate solutions, namely,…”

Section: A the Proposed Dominance Relationmentioning

confidence: 99%

A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization

Tian

Cheng

Zhang

et al. 2019

IEEE Trans. Evol. Computat.

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290

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Abstract-Both convergence and diversity are crucial to evolutionary many-objective optimization, whereas most existing dominance relations show poor performance in balancing them, thus easily leading to a set of solutions concentrating on a small region of the Pareto fronts. In this paper, a novel dominance relation is proposed to better balance convergence and diversity for evolutionary manyobjective optimization. In the proposed dominance relation, an adaptive niching technique is developed based on the angles between the candidate solutions, where only the best converged candidate solution is identified to be nondominated in each niche. Experimental results demonstrate that the proposed dominance relation outperforms existing dominance relations in balancing convergence and diversity. A modified NSGA-II is suggested based on the proposed dominance relation, which shows competitiveness against the state-of-the-art algorithms in solving many-objective optimization problems. The effectiveness of the proposed dominance relation is also verified on several other existing multi-and many-objective evolutionary algorithms.

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Section: A the Proposed Dominance Relationmentioning

confidence: 99%

A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization

Tian

Cheng

Zhang

et al. 2019

IEEE Trans. Evol. Computat.

Self Cite

290

View full text Add to dashboard Cite

show abstract

“…Motivated by simultaneously measuring the distance of the solutions to the Pareto-optimal front, and maintaining a sufficient distance between each other, Liu et al [57] proposed a many-objective evolutionary algorithm using a one-by-one selection strategy, 1by1EA for short. However, there are two issues in this algorithm.…”

Section: 3mentioning

confidence: 99%

An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat

You

Kou

2018

International Journal of Aerospace Engineering

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Multiobjective weapon-target assignment is a type of NP-complete problem, and the reasonable assignment of weapons is beneficial to attack and defense. In order to simulate a real battlefield environment, we introduce a new objective-the value of fighter combat on the basis of the original two-objective model. The new three-objective model includes maximizing the expected damage of the enemy, minimizing the cost of missiles, and maximizing the value of fighter combat. To solve the problem with complex constraints, an improved nondominated sorting algorithm III is proposed in this paper. In the proposed algorithm, a series of reference points with good performances in convergence and distribution are continuously generated according to the current population to guide the evolution; otherwise, useless reference points are eliminated. Moreover, an online operator selection mechanism is incorporated into the NSGA-III framework to autonomously select the most suitable operator while solving the problem. Finally, the proposed algorithm is applied to a typical instance and compared with other algorithms to verify its feasibility and effectiveness. Simulation results show that the proposed algorithm is successfully applied to the multiobjective weapon-target assignment problem, which effectively improves the performance of the traditional NSGA-III and can produce better solutions than the two multiobjective optimization algorithms NSGA-II and MPACO.

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“…Among them, the most representative algorithms are NSGA-II [10], SPEA-II [11], and MOEA/D [20]. And, recently, a lot of MOEAs based on GAs for solving many-objective problems were also proposed [1, 2, 12, 25, 26]. But all of them suffer from a slow convergence rate and a lot of time on generating new offspring, which are the main problems of GAs [27].…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…The most popular multiobjective evolutionary algorithms (MOEAs) are Pareto dominance based algorithms [2], such as nondominated sorting genetic algorithm II (NSGA-II) [10] and strength Pareto evolutionary algorithm II (SPEA-II) [11]. Besides the criterion of Pareto dominance, they also adopted a diversity related secondary criterion to promote a good distribution of the solutions [12]. …”

Section: Introductionmentioning

confidence: 99%

AMOBH: Adaptive Multiobjective Black Hole Algorithm

et al. 2017

Computational Intelligence and Neuroscience

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This paper proposes a new multiobjective evolutionary algorithm based on the black hole algorithm with a new individual density assessment (cell density), called “adaptive multiobjective black hole algorithm” (AMOBH). Cell density has the characteristics of low computational complexity and maintains a good balance of convergence and diversity of the Pareto front. The framework of AMOBH can be divided into three steps. Firstly, the Pareto front is mapped to a new objective space called parallel cell coordinate system. Then, to adjust the evolutionary strategies adaptively, Shannon entropy is employed to estimate the evolution status. At last, the cell density is combined with a dominance strength assessment called cell dominance to evaluate the fitness of solutions. Compared with the state-of-the-art methods SPEA-II, PESA-II, NSGA-II, and MOEA/D, experimental results show that AMOBH has a good performance in terms of convergence rate, population diversity, population convergence, subpopulation obtention of different Pareto regions, and time complexity to the latter in most cases.

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A Many-Objective Evolutionary Algorithm Using A One-by-One Selection Strategy

Cited by 245 publications

References 60 publications

A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization

A Strengthened Dominance Relation Considering Convergence and Diversity for Evolutionary Many-Objective Optimization

An Improved Nondominated Sorting Genetic Algorithm III Method for Solving Multiobjective Weapon-Target Assignment Part I: The Value of Fighter Combat

AMOBH: Adaptive Multiobjective Black Hole Algorithm

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