An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Although multiobjective particle swarm optimization (MOPSO) has good performance in solving multiobjective optimization problems, how to obtain more accurate solutions as well as improve the distribution of the solutions set is still a challenge. In this paper, to improve the convergence performance of MOPSO, an improved multiobjective quantum-behaved particle swarm optimization based on double search strategy and circular transposon mechanism (MOQPSO-DSCT) is proposed. On one hand, to solve the problem of the dramatic diversity reduction of the solutions set in later iterations due to the single search pattern used in quantum-behaved particle swarm optimization (QPSO), the double search strategy is proposed in MOQPSO-DSCT. The particles mainly learn from their personal best position in earlier iterations and then the particles mainly learn from the global best position in later iterations to balance the exploration and exploitation ability of the swarm. Moreover, to alleviate the problem of the swarm converging to local minima during the local search, an improved attractor construction mechanism based on opposition-based learning is introduced to further search a better position locally as a new attractor for each particle. On the other hand, to improve the accuracy of the solutions set, the circular transposon mechanism is introduced into the external archive to improve the communication ability of the particles, which could guide the population toward the true Pareto front (PF). The proposed algorithm could generate a set of more accurate and well-distributed solutions compared to the traditional MOPSO. Finally, the experiments on a set of benchmark test functions have verified that the proposed algorithm has better convergence performance than some state-of-the-art multiobjective optimization algorithms.

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“…set. To measure the diversity of the nondominated solutions set, SP (Spacing Metric) [38] is introduced as an indicator. Let = ( 1 , 2 , .…”

Section: Resultsmentioning

confidence: 99%

“…The size of the external archive is 100 for ZDT and DTLZ function sets. Parameters in all the algorithms are shown in [38] will be used as an indicator in this paper. IGD is the distance from the PF obtained by the algorithm to the true PF.…”

Section: Test Functions and Parameters Settingmentioning

confidence: 99%

An Improved Multiobjective Quantum‐Behaved Particle Swarm Optimization Based on Double Search Strategy and Circular Transposon Mechanism

2018

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“…The initial population is randomly generated whose size is the same as the number of weight vectors in its space. This article uses Das and Dennis's systematic method [ 27 ] to set weight vectors W _unit={ w 1 , w 2 ,…, w N }. The total number of weight vectors is equal to N = C H + M −1 M −1 , where H represents the dimension of the solution vector and M is the number of objective functions.…”

Section: The Proposed Algorithmmentioning

confidence: 99%

A Many-Objective Optimization Algorithm Based on Weight Vector Adjustment

Wang

Sun

2018

Computational Intelligence and Neuroscience

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In order to improve the convergence and distribution of a many-objective evolutionary algorithm, this paper proposes an improved NSGA-III algorithm based on weight vector adjustment (called NSGA-III-WA). First, an adaptive weight vector adjustment strategy is proposed to decompose the objective space into several subspaces. According to different subspace densities, the weight vector is sparse or densely adjusted to ensure the uniformity of the weight vector distribution on the Pareto front surface. Secondly, the evolutionary model that combines the new differential evolution strategy and genetic evolution strategy is proposed to generate new individuals and enhance the exploration ability of the weight vector in each subspace. The proposed algorithm is tested on the optimization problem of 3–15 objectives on the DTLZ standard test set and WFG test instances, and it is compared with the five algorithms with better effect. In this paper, the Whitney–Wilcoxon rank-sum test is used to test the significance of the algorithm. The experimental results show that NSGA-III-WA has a good effect in terms of convergence and distribution.

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“…Similar to the classic classification of optimization algorithms for continuous MOPs [9], the searching algorithms of CMODOPs can also be divided into the following four categories according to the principle of selecting solutions: dominance-based [10], decomposition-based [11], indicator-based [12], and hybrid selection method-based [13]. Among them, this kind of dominance-based algorithms including NSGA-II and SPEA2 are the most commonly used in CMODOPs [14,15].…”

Section: Introductionmentioning

confidence: 99%

Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems

Wang

Xiong

et al. 2021

Complex Intell. Syst.

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Surrogate-assisted optimization has attracted much attention due to its superiority in solving expensive optimization problems. However, relatively little work has been dedicated to addressing expensive constrained multi-objective discrete optimization problems although there are many such problems in the real world. Hence, a surrogate-assisted evolutionary algorithm is proposed in this paper for this kind of problem. Specifically, random forest models are embedded in the framework of the evolutionary algorithm as surrogates to improve approximate accuracy for discrete optimization problems. To enhance the optimization efficiency, an improved stochastic ranking strategy based on the fitness mechanism and adaptive probability operator is presented, which also takes into account both convergence and diversity to advance the quality of candidate solutions. To validate the proposed algorithm, it is comprehensively compared with several well-known optimization algorithms on several benchmark problems. Numerical experiments are demonstrated that the proposed algorithm is very promising for the expensive constrained multi-objective discrete optimization problems.

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An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Cited by 16 publications

References 28 publications

An Improved Multiobjective Quantum‐Behaved Particle Swarm Optimization Based on Double Search Strategy and Circular Transposon Mechanism

An Improved Multiobjective Quantum‐Behaved Particle Swarm Optimization Based on Double Search Strategy and Circular Transposon Mechanism

A Many-Objective Optimization Algorithm Based on Weight Vector Adjustment

Surrogate-assisted evolutionary algorithm for expensive constrained multi-objective discrete optimization problems

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