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Dynamic Multiobjectives Optimization With a Changing Number of Objectives

Existing studies on dynamic multiobjective optimization (DMO) focus on problems with time-dependent objective functions, while the ones with a changing number of objectives have rarely been considered in the literature. Instead of changing the shape or… Continue reading

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Localized Weighted Sum Method for Many-Objective Optimization

Decomposition via scalarization is a basic concept for multiobjective optimization. The weighted sum (WS) method, a frequently used scalarizing method in decomposition-based evolutionary multiobjective (EMO) algorithms, has good features such as computationally easy and high search efficiency, compared to other scalarizing methods. However, it is often criticized by the loss of effect on nonconvex problems. This paper seeks to utilize advantages of the WS method, without suffering from its disadvantage, to solve many-objective problems. A novel decomposition-based EMO algorithm called multiobjective evolutionary algorithm based on decomposition LWS (MOEA/D-LWS) is proposed in which the WS method is applied in a local manner. That is, for each search direction, the optimal solution is selected only amongst its neighboring solutions. The neighborhood is defined using a hypercone. The apex angle of a hypervcone is determined automatically in a priori. The effectiveness of MOEA/D-LWS is demonstrated by comparing it against three variants of MOEA/D, i.e., MOEA/D using Chebyshev method, MOEA/D with an adaptive use of WS and Chebyshev method, MOEA/D with a simultaneous use of WS and Chebyshev method, and four state-of-the-art many-objective EMO algorithms, i.e., preference-inspired co-evolutionary algorithm, hypervolume-based evolutionary, $boldsymbol {theta }$ -dominance-based algorithm, and SPEA2+SDE for the WFG benchmark problems with up to seven conflicting objectives. Experimental results show that MOEA/D-LWS outperforms the comparison algorithms for most of test problems, and is a competitive algorithm for many-objective optimization. Continue reading

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A Decision Variable Clustering-Based Evolutionary Algorithm for Large-Scale Many-Objective Optimization

The current literature of evolutionary many-objective optimization is merely focused on the scalability to the number of objectives, while little work has considered the scalability to the number of decision variables. Nevertheless, many real-world pro… Continue reading

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Guest Editorial Evolutionary Many-Objective Optimization

Over the past two decades, evolutionary algorithms have successfully been applied to single and multiobjective optimization problems having up to three objectives. Compared to traditional mathematical programming techniques, evolutionary multiobjective… Continue reading

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A Surrogate-Assisted Reference Vector Guided Evolutionary Algorithm for Computationally Expensive Many-Objective Optimization

We propose a surrogate-assisted reference vector guided evolutionary algorithm (EA) for computationally expensive optimization problems with more than three objectives. The proposed algorithm is based on a recently developed EA for many-objective optim… Continue reading

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IEEE Transactions on Evolutionary Computation publication information

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IEEE Transactions on Evolutionary Computation Society Information

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Deadline extended for Special Issue on Genetic Programming, Evolutionary Computation and Visualization

The deadline for submissions to the Special Issue on Genetic Programming, Evolutionary Computation and Visualization (Guest Editors: Nadia Boukhelifa and Evelyne Lutton) has been extended to January 22, 2018. The call for papers is here. Continue reading

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Problem Features versus Algorithm Performance on Rugged Multiobjective Combinatorial Fitness Landscapes

Evolutionary Computation, Volume 25, Issue 4, Page 555-585, Winter 2017. <br/> Continue reading

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Expected Fitness Gains of Randomized Search Heuristics for the Traveling Salesperson Problem

Evolutionary Computation, Volume 25, Issue 4, Page 673-705, Winter 2017. <br/> Continue reading

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