Evolutionary computation (EC)Co-evolutionary multi-population evolutionary algorithm (CMEA)Dynamic multiobjective optimization problems (DMOPs) widely appear in various real-world applications and have attracted increasing attention worldwide. However, how to obtain both good population diversity and fast ...
Multi-population evolutionary and swarm intelligence dynamic optimization algorithms are the most flexible and effective methods for solving dynamic optimization problems. In a dynamic optimization problem, the search space is affected by environmental changes over time. In multi-population evolutionary and ...
The multi-population approaches are found effective while dealing with various problems and these have outperformed the existing fixed population size methods for different problems. A self-organizing scout's multi-population evolutionary algorithm was proposed for the dynamic optimization problems [8]. A...
This paper proposes a dynamic multipopulation evolutionary framework (DMOEF-MS), which integrates Steffensen’s method into a novel multipopulation framework to solve DMOPs. In this section, the motivation and framework of the proposed algorithm are given, followed by a detailed description of the ...
In this section, Hybrid Selection based Multi/Many-Objective Evolutionary Algorithm (HS-MOEA) is introduced. The different steps of HS-MOEA are detailed below. Initialization A set of uniform weight vectors (\(w\)) are generated using the NBI method, then subsequently, a population of size \...
Based on this theory, a Multilevel Evolutionary Optimization algorithm (MLEO) is presented. In MLEO, a species is subdivided in cooperative populations and then each population is subdivided in groups, and evolution occurs at two levels so called individual and group levels. A fast population ...
The main population is divided into several sub-populations where each sub-population is optimized based on similar/different selection criteria. If different selection criteria are applied, it may refer to either different objective component or utility function of the objectives. The shuffling and...
A Multi-Variation Multifactorial Evolutionary Algorithm for Large-Scale Multi-Objective Optimization 传送门 摘要 For solving large-scale multi-objective problems (LSMOPs), the transformation-based methods have shown promising search efficiency, which varies the original problem as a new simplified problem ...
MOEDO algorithm does not require to set any new parameter other than the usual EDO parameters such as the population size, termination parameter and their associated parameters. The flow chart of MOEDO algorithm can be shown in Fig. 4. Figure 3 The procedure of the NDS approach based on ...
1) Multi-population Co-evolutionary Algorithm 多种群协同算法2) multi-swarm cooperative particle swarm optimization algorithm 多种群协同粒子群优化算法 1. An improved multi-swarm cooperative particle swarm optimization algorithm was presented to solve the proposed optimal power flow. 最优潮流的求解采用...