Abstract Multiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to selection inappropriate leaders inefficient evolution strategies. Therefore, circumvent rapid loss population premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective using fusion learning strategies (KGMOPSO), which an improved...

Given the growth of wireless networks and increase advantages applications communication networks, especially mobile ad hoc (MANETs), this type network has attracted attention users researchers more than before. The benefit these types in various kinds environments is that MANET does not require to hardware infrastructure communicate send receive data packets within network. It one main reasons...

Reducing energy consumption and maintenance costs of a pumping system is seen as an important but difficult multi-objective optimization problem. Many evolutionary algorithms, such particle swarm (PSO), (MOPSO), non-dominated sorting genetic algorithm II (NSGA-II) have been used. However, lack comparison between these approaches poses challenge to the selection approach for stormwater drainage ...

In extending the Particle Swarm Optimisation methodology to multi-objective problems it is unclear how global guides for particles should be selected. Previous work has relied on metric information in objective space, although this is at variance with the notion of dominance which is used to assess the quality of solutions. Here we propose methods based exclusively on dominance for selecting gu...

Particle Swarm Optimizers (PSOs) have been applied to solve Multi-Objective Optimization Problems (MOPs) for its successful applications in solving single objective optimization problems and are named as Multi-Objective PSOs (MOPSOs). However, MOPSOs are often trapped in local optima, cost more function evaluations and suffer from the curse of dimensionality. A cooperative coevolutionary and ε-...