Enhancing A Multiobjective Evolutionary Algorithm Through Flexible Evolution
نویسندگان
چکیده
In this paper the use of a powerful single-objective optimization methodology in Multi-objective Optimization Algorithms (MOEAs) is introduced. The Flexible Evolution concepts (FE) have been recently developed and proved its efficiency gains compared with several Evolutionary Algorithms solving single-objective challenging problems. The main feature of such concepts is the flexibility to self-adapt the internal behaviour of the algorithm to optimize its search capacity. In this paper we present the first attempt to incorporate FE into MOEAs. A real coded NSGA-II algorithm was modified replacing the crossover and mutation operators with the Sampling Engine of FE. Other two FE characteristics were implemented too: The Probabilistic Control Mechanism and the Enlarged Individual’s Code. The performance of the resulting algorithm has been compared with the classical NSGA-II using several test functions. The results obtained and presented show that FE_based algorithms have advantages over the classical ones, especially when optimizing highly multimodal complex functions.
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 کاملDeveloping Adaptive Differential Evolution as a New Evolutionary Algorithm, Application in Optimization of Chemical Processes
متن کامل
Solving Multiobjective Optimization Problems using Evolutionary Algorithm
Being capable of finding a set of pareto–optimal solutions in a single run, which is a necessary feature for multi–criteria decision making, Evolutionary Algorithms (EAs) has attracted many researchers and practitioners to address the solution of Multiobjective Optimization Problems (MOPs). In a previous work, we developed a Pareto Differential Evolution (PDE) algorithm to handle multiobjective...
متن کاملDEMO: Differential Evolution for Multiobjective Optimization
Differential Evolution (DE) is a simple but powerful evolutionary optimization algorithm with many successful applications. In this paper we propose Differential Evolution for Multiobjective Optimization (DEMO) – a new approach to multiobjective optimization based on DE. DEMO combines the advantages of DE with the mechanisms of Paretobased ranking and crowding distance sorting, used by state-of...
متن کاملA COMPARISON OF MULTIOBJECTIVE EVOLUTIONARY ALGORITHMS by Crina Gro ş
In this paper a comparison of the most recent algorithms for Multiobjective Optimization is realized. For this comparison are used the followings algorithms: Strength Pareto Evolutionary Algorithm (SPEA), Pareto Archived Evolution Strategy (PAES), Nondominated Sorting Genetic Algorithm (NSGA II), Adaptive Pareto Algorithm (APA). The comparison is made by using five test functions.
متن کامل