Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Multiobjective Evolutionary Algorithms and Applications (Advanced Information and Knowledge Processing)
Proceedings of the 10th annual conference on Genetic and evolutionary computation
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Theory of the hypervolume indicator: optimal μ-distributions and the choice of the reference point
Proceedings of the tenth ACM SIGEVO workshop on Foundations of genetic algorithms
Multiplicative approximations and the hypervolume indicator
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Capabilities of EMOA to detect and preserve equivalent pareto subsets
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Substitute distance assignments in NSGA-II for handling many-objective optimization problems
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Pareto-, aggregation-, and indicator-based methods in many-objective optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
Integrating decision space diversity into hypervolume-based multiobjective search
Proceedings of the 12th annual conference on Genetic and evolutionary computation
DBSCAN-based multi-objective niching to approximate equivalent pareto-subsets
Proceedings of the 12th annual conference on Genetic and evolutionary computation
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part II
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
On the Evolutionary Optimization of Many Conflicting Objectives
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
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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-objective optimization (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.