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IEEE Transactions on Evolutionary Computation information for authors

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A Scalability Study of Many-Objective Optimization Algorithms

Over the past few decades, a plethora of computational intelligence algorithms designed to solve multiobjective problems have been proposed in the literature. Unfortunately, it has been shown that a large majority of these optimizers experience performance degradation when tasked with solving problems possessing more than three objectives, referred to as many-objective problems (MaOPs). The downfall of these optimizers is that simultaneously maintaining a uniformly-spread set of solutions along with appropriate selection pressure to converge toward the Pareto-optimal front becomes significantly difficult as the number of objectives increases. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs with a large number of decision variables. In this paper, insight is given into the current state of many-objective research by investigating scalability of state-of-the-art algorithms using 3–15 objectives and 30–1000 decision variables. Results indicate that evolutionary optimizers are generally the best performers when the number of decision variables is low, but are outperformed by the swarm intelligence optimizers in several large-scale MaOP instances. However, a recently proposed evolutionary algorithm which combines dominance and subregion-based decomposition is shown to be promising for handling the immense search spaces encountered in large-scale MaOPs. Continue reading

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IEEE Congress on Evolutionary Computation

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Multiline Distance Minimization: A Visualized Many-Objective Test Problem Suite

Studying the search behavior of evolutionary many-objective optimization is an important, but challenging issue. Existing studies rely mainly on the use of performance indicators which, however, not only encounter increasing difficulties with the numbe… Continue reading

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Turning High-Dimensional Optimization Into Computationally Expensive Optimization

Divide-and-conquer (DC) is conceptually well suited to deal with high-dimensional optimization problems by decomposing the original problem into multiple low-dimensional subproblems, and tackling them separately. Nevertheless, the dimensionality mismat… Continue reading

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A Set-Based Genetic Algorithm for Interval Many-Objective Optimization Problems

Interval many-objective optimization problems (IMaOPs), involving more than three objectives and at least one subjected to interval uncertainty, are ubiquitous in real-world applications. However, there have been very few effective methods for solving … Continue reading

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An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing

Virtual machine placement (VMP) and energy efficiency are significant topics in cloud computing research. In this paper, evolutionary computing is applied to VMP to minimize the number of active physical servers, so as to schedule underutilized servers… Continue reading

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Particle Swarm Optimization With a Balanceable Fitness Estimation for Many-Objective Optimization Problems

Recently, it was found that most multiobjective particle swarm optimizers (MOPSOs) perform poorly when tackling many-objective optimization problems (MaOPs). This is mainly because the loss of selection pressure that occurs when updating the swarm. The number of nondominated individuals is substantially increased and the diversity maintenance mechanisms in MOPSOs always guide the particles to explore sparse regions of the search space. This behavior results in the final solutions being distributed loosely in objective space, but far away from the true Pareto-optimal front. To avoid the above scenario, this paper presents a balanceable fitness estimation method and a novel velocity update equation, to compose a novel MOPSO (NMPSO), which is shown to be more effective to tackle MaOPs. Moreover, an evolutionary search is further run on the external archive in order to provide another search pattern for evolution. The DTLZ and WFG test suites with 4–10 objectives are used to assess the performance of NMPSO. Our experiments indicate that NMPSO has superior performance over four current MOPSOs, and over four competitive multiobjective evolutionary algorithms (SPEA2-SDE, NSGA-III, MOEA/DD, and SRA), when solving most of the test problems adopted. Continue reading

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Table of contents

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On the Performance Degradation of Dominance-Based Evolutionary Algorithms in Many-Objective Optimization

In the last decade, it has become apparent that the performance of Pareto-dominance-based evolutionary multiobjective optimization algorithms degrades as the number of objective functions of the problem, given by ${n}$ , grows. This performance degrad… Continue reading

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