Mobes: a Multiobjective Evolution Strategy for Constrained Optimization Problems
نویسندگان
چکیده
In this paper a new MultiOBjective Evolution Strategy (MOBES) for solving multi-objective optimization problems subject to linear and nonlinear constraints is presented. MOBES is based on the new concept of C-, F-and N-tness, which allows systematically to handle constraints and (in)feasible individuals. The existence of niche infeasible individuals in every population enables to explore new areas of the feasible region and new feasible pareto-optimal solutions. Moreover, MOBES proposed a new selection algorithm for searching, maintaining a set of feasible pareto-optimal solutions in every generation. The performance of the MOBES can be successfully evaluated on two selected test problems.
منابع مشابه
Multiobjective Imperialist Competitive Evolutionary Algorithm for Solving Nonlinear Constrained Programming Problems
Nonlinear constrained programing problem (NCPP) has been arisen in diverse range of sciences such as portfolio, economic management etc.. In this paper, a multiobjective imperialist competitive evolutionary algorithm for solving NCPP is proposed. Firstly, we transform the NCPP into a biobjective optimization problem. Secondly, in order to improve the diversity of evolution country swarm, and he...
متن کاملA Hybrid of Differential Evolution and Genetic Algorithm for Constrained Multiobjective Optimization Problems
Two novel schemes of selecting the current best solutions for multiobjective differential evolution are proposed in this paper. Based on the search biases strategy suggested by Runarsson and Yao, a hybrid of multiobjective differential evolution and genetic algorithm with (N+N) framework for constrained MOPs is given. And then the hybrid algorithm adopting the two schemes respectively is compar...
متن کاملQuasi-Newton Methods for Nonconvex Constrained Multiobjective Optimization
Here, a quasi-Newton algorithm for constrained multiobjective optimization is proposed. Under suitable assumptions, global convergence of the algorithm is established.
متن کاملConstrained Multi-Objective Optimization Algorithm with Ensemble of Constraint Handling Methods
Different constraint handling techniques have been used with multiobjective evolutionary algorithms (MOEA) to solve constrained multiobjective optimization problems. It is impossible for a single constraint handling technique to outperform all other constraint handling techniques always on every problem irrespective of the exhaustiveness of parameter tuning. To overcome this selection problem, ...
متن کاملAdding a diversity mechanism to a simple evolution strategy to solve constrained optimization problems
AbstractIn this paper, we propose the use of a Simple Evolution Strategy (SES) (i.e., a -ES with self-adaptation that uses three tournament rules based on feasibility) coupled with a diversity mechanism to solve constrained optimization problems. The proposed mechanism is based on multiobjective optimization concepts taken from an approach called the Niched-Pareto Genetic Algorithm (NPGA). The ...
متن کامل