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...

The aim of this study is to propose simple and reliable techniques to assess the condition of metal oxide arrester based on the dielectric response techniques. A number of modern electrical diagnostics for Metal Oxide Surge Arrester (MOSA) are discussed in this paper. The techniques included return voltage, decay voltage and polarisation/depolarisation current measurement. The single and multip...

In this paper, we proposed an algorithm for solving the problem of task scheduling using particle swarm optimization algorithm, with changes in the Selection and removing the guide and also using the technique to get away from the bad, to move away from local extreme and diversity. Scheduling algorithms play an important role in grid computing, parallel tasks Scheduling and sending them to ...

this paper explores the capabilities of multi-objective particle swarm optimization algorithmin a simulation-optimization model for solving waste load allocation problems. the main goals are totaltreatment costs, violation of the water quality standards and equity. in this research, the water qualitysimulation model is coupled with a multi-objective optimization model, mopso. in order to derive...

The inability of conventional energy sources to fully meet the rapidly increasing energy demands in today’s world has led to the growing importance of hybrid power generation systems that incorporate renewable energy sources. This work proposes an optimally designed multi-source standalone hybrid generation system comprising of photovoltaic panels, wind turbine generators, batteries and diesel ...

One of the most important aspects affecting the performance of a supply chain is the management of inventories. Managing inventory in complex supply chains is typically difficult, and may have a significant impact on the customer service level and system-wide costs. The main challenge of inventory management is that almost every inventory problem involves multiple and conflicting objectives tha...

Integrated production-distribution planning (PDP) is one of the most important approaches in supply chain networks. We consider a supply chain network (SCN) consististing of multi suppliers, plants, distribution centers (DCs), and retailers. A bi-objective mixed integer linear programming model for integrating production-distribution designed here aim to simultaneously minimize total net costs ...

This chapter presents a Discrete Multi-objective Particle Swarm Optimization (MOPSO) algorithm that determines the optimal order of activities execution within a design project that minimizes project total iterative time and cost. Numerical Design Structure Matrix (DSM) was used to model project activities’ execution order along with their interactions providing a base for calculating the objec...

This study compares a number of selection regimes for the choosing of global best (gbest) and personal best (pbest) for swarm members in multi-objective particle swarm optimisation (MOPSO). Two distinct gbest selection techniques are shown to exist in the literature, those that do not restrict the selection of archive members and those with ‘distance’ based gbest selection techniques. Theoretic...

A major difficulty in the use of metaheuristics (i.e. evolutionary and particle swarm algorithms) to deal with multi-objective optimization problems is the choice of a convenient point at which to stop computation. Indeed, it is difficult to find the best compromise between the stopping criterion and the algorithm performance. This paper addresses this issue using the Non-dominated Sorting Gene...