Evolutionary algorithms have been widely applied for solving dynamic constrained optimization problems (DCOPs) as a common area of research in evolutionary optimization. Current benchmarks proposed for testing these problems in the continuous spaces are either not scalable in problem dimension or the settings for the environmental changes are not flexible. Moreover, they mainly focus on non-linear environmental changes on the objective function. While the dynamism in some real-world problems exists in the constraints and can be emulated with linear constraint changes. The purpose of this paper is to introduce a framework which produces benchmarks in which a dynamic environment is created with simple changes in linear constraints (rotation and translation of constraint's hyperplane). Our proposed framework creates dynamic benchmarks that are flexible in terms of number of changes, dimension of the problem and can be applied to test any objective function. Different constraint handling techniques will then be used to compare with our benchmark. The results reveal that with these changes set, there was an observable effect on the performance of the constraint handling techniques.
Systematic Derivation of Behaviour Characterisations in Evolutionary Robotics
Instituto de Telecomunicações（葡萄牙电信研究所）；Faculdade de Ciências da Universidade de Lisboa（里斯本大学理学院）；Instituto Universitário de Lisboa (ISCTE-IUL)（里斯本大学学院）
An Efficient Reconstructed Differential Evolution Variant by Some of the Current State-of-the-art Strategies for Solving Single Objective Bound Constrained Problems
Faculty of Engineering University of Toyama Toyama, Japan；School of Mechanical Engineering Tongji University Shanghai, China
