Extension of ∆ p SMS - EMOA for 3 - D benchmark functions
نویسندگان
چکیده
منابع مشابه
Surrogate-Assisted Partial Order-Based Evolutionary Optimisation
In this paper, we propose a novel approach (SAPEO) to support the survival selection process in multi-objective evolutionary algorithms with surrogate models it dynamically chooses individuals to evaluate exactly based on the model uncertainty and the distinctness of the population. We introduce variants that differ in terms of the risk they allow when doing survival selection. Here, the anytim...
متن کاملA Parallel Version of SMS-EMOA for Many-Objective Optimization Problems
In the last decade, there has been a growing interest in multiobjective evolutionary algorithms that use performance indicators to guide the search. A simple and effective one is the S-Metric Selection Evolutionary Multi-Objective Algorithm (SMS-EMOA), which is based on the hypervolume indicator. Even though the maximization of the hypervolume is equivalent to achieving Pareto optimality, its c...
متن کاملEvolutionary Many-Objective Optimization Based on Kuhn-Munkres' Algorithm
In this paper, we propose a new multi-objective evolutionary algorithm (MOEA), which transforms a multi-objective optimization problem into a linear assignment problem using a set of weight vectors uniformly scattered. Our approach adopts uniform design to obtain the set of weights and Kuhn-Munkres’ (Hungarian) algorithm to solve the assignment problem. Differential evolution is used as our sea...
متن کامل“Multiobjective Optimization of Water Distribution Networks Using SMS-EMOA”
The multiobjective evolutionary algorithm SMS-EMOA was shown by Emmerich et al. to outperform the well-established NSGA-II on a range of common test problems, using the S metric as comparison criterion. This study assesses which of the two algorithms performs best with respect to the optimization of water distribution networks, using the unconstrained three objective reformulation by Formiga et...
متن کاملHypervolume based metaheuristics for multiobjective optimization
The purpose of multiobjective optimization is to find solutions that are optimal regarding several goals. In the branch of vector or Pareto optimization all these goals are considered to be of equal importance, so that compromise solutions that cannot be improved regarding one goal without deteriorating in another are Paretooptimal. A variety of quality measures exist to evaluate approximations...
متن کامل