This chapter presents a paper that proposes a multiobjective evolutionary algorithm (MOEA) for structural optimization. The proposed approach emphasizes on efficiency and has been found to be competitive with respect to other MOEAs in current use. Evolutionary algorithms have become an increasingly ...
多目标进化算法(Multi-Objective Evolutionary Algorithms,简称MOEAs)是一类用于解决多目标优化问题的进化算法。多目标优化问题(Multi-Objective Optimization Problems,简称MOPs)涉及多个目标函数,这些目标往往是相互冲突的,因此不可能同时达到最优。多目标优化的目的是找到一组“帕累托最优解”(Pareto optimal solutions),在...
Multi-objective evolutionary algorithms (MOEAs) are well-suited for solving several complex multi-objective problems with two or three objectives. However, as the number of conflicting objectives increases, the performance of most MOEAs is severely deteriorated. How to improve MOEAs閳 erformance when ...
We consider the usage of evolutionary algorithms for multiobjective programming (MOP), i.e. for decision problems with alternatives taken from a real-valued vector space and evaluated according to a vector-valued objective function. Selection mechanisms, possibilities of temporary fitness deterioration, ...
Multi-objective evolutionary algorithms (MOEAs) have gained popularity as effective a posteriori technique for addressing MOPs. Their strength lies in their ability to approximate the Pareto set (PS) and/or Pareto front (PF) within a single execution21. A standard MOEA operates in two main stages...
Multi-objective evolutionary algorithms (MOEAs) are any of the paradigms of evolutionary computing (e.g., genetic algorithms, evolutionary strategies, etc.) used to solve problems requiring optimization of two or more potentially conflicting objectives, without resorting to the reduction of the objective...
Abstract—Multiobjective evolutionary algorithms (EAs) that use nondominated sorting and sharing have been criticized mainly for their: 1) O(MN3) computational complexity (where is the number of objectives and is the population size); 2) nonelitism approach; and ...
current state-of-the-art multiobjective evolutionary algorithms (MOEAs) [2]–[4], [12]–[19]. These algorithms treat a MOP as a whole. They do not associate each individual solution with any particular scalar optimization problem. In a scalar objective optimization problem, all the solutions ...
To solve the MPOs problem, many excellent multi-objective evolutionary algorithms (MOEAs) have been proposed [[1], [2], [3], [4], [5], [6], [7], [8]]. These MOEAs are capable of generating solution sets that approximate the Pareto frontier, and these solution sets exhibit good ...
Multiobjective evolutionary algorithms (MOEAs) have faced the challenge of balancing diversity and convergence in dealing with many-objective optimization problems (MaOPs). Most of them use a series of strategies to increase the selection pressure among solutions for convergence promotion, or additional...