Recently it has been pointed out in many studies that evolutionary multi-objectiveoptimization (EMO) algorithms with Pareto dominance-based fitness evaluation do not work well on many-objectiveproblems with four or ...
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ISBN:
(纸本)9781424478354
Recently it has been pointed out in many studies that evolutionary multi-objectiveoptimization (EMO) algorithms with Pareto dominance-based fitness evaluation do not work well on many-objectiveproblems with four or more objectives. In this paper, we examine the behavior of well-known and frequently-used EMO algorithms such as NSGA-II, SPEA2 and MOEA/D on many-objectiveproblems with correlated or dependent objectives. First we show that good results on many-objective 0/1 knapsack problems with randomly generated objectives are not obtained by Pareto dominance-based EMO algorithms (i.e., NSGA-II and SPEA2). Next we show that the search ability of NSGA-II and SPEA2 is not degraded by the increase in the number of objectives when they are highly correlated or dependent. In this case, the performance of MOEA/D is deteriorated. As a result, NSGA-II and SPEA2 outperform MOEA/D with respect to the convergence of solutions toward the Pareto front for some many-objectiveproblems. Finally we show that the addition of highly correlated or dependent objectives can improve the performance of EMO algorithms on two-objectiveproblems in some cases.
This paper presents an analysis of parameters defined within some of the most representative decomposition-based multi-objective evolutionary algorithms (MOEAs) namely: MOEA/D, MOEA/D-DE, and MOEA/D-DRA. Our main inte...
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ISBN:
(纸本)9781509060177
This paper presents an analysis of parameters defined within some of the most representative decomposition-based multi-objective evolutionary algorithms (MOEAs) namely: MOEA/D, MOEA/D-DE, and MOEA/D-DRA. Our main interest is focused in the many-objective context, where decompositionbased MOEAs have been successfully applied, but a lack of analysis on the relevance of their parameters is evidently observable. We review the literature related to those parameter values that have been commonly adopted and we perform some experiments oriented to validate these decisions. Our results show that some alternative parameter configurations can allow these methods to obtain better solutions than the standard values. Moreover, some of our recommendations can conduct to inspect in detail the design of these algorithms.
A many-objectiveproblems (MaOP) refer to the optimization problem involving more than three objectives. Particle swarm optimization (PSO) is one of the potential heuristic methods suited for solving MaOPs. The person...
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ISBN:
(纸本)9781509060177
A many-objectiveproblems (MaOP) refer to the optimization problem involving more than three objectives. Particle swarm optimization (PSO) is one of the potential heuristic methods suited for solving MaOPs. The personal best selection strategy, the global best selection strategy, and the archive maintenance strategy are the three key components in the design of a many-objective Particle Swarm optimization (MaOPSO). The personal best and global best selection strategies determine the direction where particles will fly. The archive maintenance strategy has an important impact on convergence and diversity of its algorithm. In MaOPs, the high dimensionality in the objective space decreases the probability of a solution to be dominated by the other solutions in the population. Thus, it becomes more difficult for PSO to select the good leaders from so many non-dominated solutions. In this paper, a virtual Inverted Generational Distance indicator is proposed to evaluate the comprehensive quality of a solution in the external archive according to a constructed virtual Pareto front (vPF). Accordingly, a new indicator-based MaOPSO using vPF (MaOPSO/vPF) is developed to improve the convergence and diversity of the approximate Pareto front. Experimental results on the MaF test suites demonstrate that the proposed MaOPSO/vPF performs better than some selected competing Multi-objectiveoptimization Evolutionary Algorithms.
Pareto Dominance-based Multi-objective Evolutionary algorithms (PDMOEAs) have issues while handling many-objective optimization problems (MaOPs) due to the lack of selection pressure provided by the Pareto dominance t...
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ISBN:
(纸本)9781538692769
Pareto Dominance-based Multi-objective Evolutionary algorithms (PDMOEAs) have issues while handling many-objective optimization problems (MaOPs) due to the lack of selection pressure provided by the Pareto dominance to guide the search process towards the convergence. Hence, most of the PDMOEAs proposed rely on additional selection criterion to establish preferences between the solutions. In this paper, we propose a PDMOEA with multiple ranking methods (PDMOEA-MR), an extension to the proposed PDMOEA with ranking methods for MaOPs which assigns priority rank based upon Ranking methods and niche radius. In PDMOEA with ranking methods for MaOPs, ranking methods such as Average rank (AR) in PDMOEA-AR, and weighted sum of objectives (WS) in PDMOEA-WS, are used. Instead of using two ranking methods separately, in the proposed PDMOEA_MR, both the ranking methods AR and WS are incorporated into a common framework and a different strategy is adopted to assign priority rank. The performance of proposed method is analyzed on 16 test problems.
Cloud computing is able to deliver large amount of computing resources on demand, and it has become one of the most effective ways to implement large-scale computationally intensive applications. In a cloud computing ...
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Cloud computing is able to deliver large amount of computing resources on demand, and it has become one of the most effective ways to implement large-scale computationally intensive applications. In a cloud computing environment, applications typically involve workflows. Therefore, optimized workflow scheduling can greatly improve the overall performance of cloud computing. However, existing studies on cloud workflow scheduling usually consider at most three objectives only and effective methods to solve scheduling problems with four or more objectives still lack. To address the above issue, a new cloud workflow scheduling model is formulated that simultaneously considers four objectives, namely, minimization of makespan, minimization of the average execution time of all workflow instances, maximization of reliability, and minimization of the cost of workflow execution. To solve this four-objective scheduling problem, an improved knee point driven evolutionary algorithm is proposed. Extensive experimental results demonstrate that the improved algorithm outperforms existing popular many-objective evolutionary algorithms in most experimental scenarios studied in this work, in particular when there is sufficiently large amount of computing resource supply and the time for scheduling is limited. (C) 2017 Elsevier B.V. All rights reserved.
In many-objectiveoptimization, the balance between convergence and diversity is hard to maintain, while the dominance resistant solutions (DRSs) could further harm the balance particularly in high-dimensional objecti...
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In many-objectiveoptimization, the balance between convergence and diversity is hard to maintain, while the dominance resistant solutions (DRSs) could further harm the balance particularly in high-dimensional objective space. Thus, this paper proposes a novel selection strategy - boundary elimination selection based on binary search (called BESBS), trying to avoid the impact of DRSs during the optimization and achieve a good balance between the convergence and diversity simultaneously. During the environmental selection, the binary search (BS) is used to adaptively adjust the epsilon value in the epsilon-dominance relationship and assist in detecting the well-distributed neighbors for the elite solutions. Then the epsilon value obtained by BS is used for serving the boundary elimination selection (BES) to guarantee the stability of the elite population. To improve the convergence, BES is mainly designed to select individuals approximating to the ideal point. By modifying the fitness of solutions and choosing solutions in terms of the shuffled sequence of objective axis, the DRSs will be eliminated during the selection. Thus, BESBS could achieve a good balance between the convergence and diversity and avoid the impact from DRSs simultaneously. From a series of experiments with 35 instances, the experimental results have shown that BESBS is competitive against 8 state-of-art many-objective evolutionary algorithms. (C) 2017 Elsevier B.V. All rights reserved.
The convergence and the diversity are two main goals of an evolutionary algorithm for many-objective optimization problems. However, achieving these two goals simultaneously is the difficult and challenging work for m...
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The convergence and the diversity are two main goals of an evolutionary algorithm for many-objective optimization problems. However, achieving these two goals simultaneously is the difficult and challenging work for multi-objective evolutionary algorithms. A uniform evolutionary algorithm based on decomposition and the control of dominance area of solutions (CDAS) is proposed to achieve these two goals. Firstly, a uniform design method is utilized to generate the weight vectors whose distribution is uniform over the design space, then the initial population is classified into some sub-populations by these weight vectors. Secondly, an update strategy based on decomposition is proposed to maintain the diversity of obtained solutions. Thirdly, to improve the convergence, a crossover operator based on the uniform design method is constructed to enhance the search capacity and the CDAS is used to sort solutions of each sub-population to guide the search process to converge the Pareto optimal solutions. Moreover, the proposed algorithm compare with some efficient state-of-the-art algorithms, e.g., NSGAII-CDAS, MOEA/D, UMOEND and HypE, on six benchmark functions with 5-25 objectives are made, and the results indicate that the proposed algorithm is able to obtain solutions with better convergence and diversity. (C) 2015 Elsevier B.V. All rights reserved.
For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition based evolutionary algorithm with uni...
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For many-objective optimization problems, how to get a set of solutions with good convergence and diversity is a difficult and challenging work. In this paper, a new decomposition based evolutionary algorithm with uniform designs is proposed to achieve the goal. The proposed algorithm adopts the uniform design method to set the weight vectors which are uniformly distributed over the design space, and the size of the weight vectors neither increases nonlinearly with the number of objectives nor considers a formulaic setting. A crossover operator based on the uniform design method is constructed to enhance the search capacity of the proposed algorithm. Moreover, in order to improve the convergence performance of the algorithm, a sub-population strategy is used to optimize each sub-problem. Comparing with some efficient state-of-the-art algorithms, e.g., NSGAII-CE, MOEA/D and HypE, on six benchmark functions, the proposed algorithm is able to find a set of solutions with better diversity and convergence. (C) 2015 Elsevier B.V. All rights reserved.
Recently distance minimization problems in a two-dimensional decision space have been utilized as many-objective test problems to visually examine the behavior of evolutionary multi-objectiveoptimization (EMO) algori...
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ISBN:
(纸本)9781450305570
Recently distance minimization problems in a two-dimensional decision space have been utilized as many-objective test problems to visually examine the behavior of evolutionary multi-objectiveoptimization (EMO) algorithms. Such a test problem is usually defined by a single polygon where the distance from a solution to each vertex is minimized in the decision space. We can easily generate different test problems from different polygons. We can also easily generate test problems with multiple equivalent Pareto optimal regions using multiple polygons of the same shape and the same size. Whereas these test problems have a number of advantages, they have no clear relevance to real-world situations since they are artificially generated unrealistic test problems. In this paper, we generate a distance minimization problem from a real-world map. Our test problem has four objectives, which are to minimize the distances to the nearest elementary school, junior high school, railway station, and convenience store. Using our test problem, we examine the behavior of well-known and frequently-used EMO algorithms in terms of their diversity maintenance ability in the two-dimensional decision space.
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