scholarly journals An Improved Hybrid Particle Swarm Optimization and Tabu Search Algorithm for Expansion Planning of Large Dimension Electric Distribution Network

Energies ◽  
2019 ◽  
Vol 12 (16) ◽  
pp. 3052 ◽  
Author(s):  
Ali Ahmadian ◽  
Ali Elkamel ◽  
Abdelkader Mazouz
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Combinatorial Problem ◽  
Distribution Networks ◽  
Swarm Optimization ◽  
Expansion Planning ◽  
Hybrid Particle Swarm Optimization ◽  
Electric Distribution

Optimal expansion of medium-voltage power networks is a common issue in electrical distribution planning. Minimizing the total cost of the objective function with technical constraints make it a combinatorial problem which should be solved by powerful optimization algorithms. In this paper, a new improved hybrid Tabu search/particle swarm optimization algorithm is proposed to optimize the electric expansion planning. The proposed method is analyzed both mathematically and experimentally and it is applied to three different electric distribution networks as case studies. Numerical results and comparisons are presented and show the efficiency of the proposed algorithm. As a result, the proposed algorithm is more powerful than the other algorithms, especially in larger dimension networks.

scholarly journals Hybrid particle swarm optimization and tabu search for the design of large-scale water distribution networks

RBRH ◽  
2021 ◽  
Vol 26 ◽  
Author(s):  
José Eloim Silva de Macêdo ◽  
José Roberto Gonçalves de Azevedo ◽  
Saulo de Tarso Marques Bezerra
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Water Distribution ◽  
Large Scale ◽  
Particle Swarm ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Swarm Optimization ◽  
Hybrid Particle ◽  
Hybrid Particle Swarm Optimization

ABSTRACT Water distribution network (WDN) optimization has received special attention from various technicians and researchers, mainly due to its high costs of implementation, operation and maintenance. However, the low computational efficiency of most developed algorithms makes them difficult to apply in large-scale WDN design problems. This article presents a hybrid particle swarm optimization and tabu search (H-PSOTS) algorithm for WDN design. Incorporating tabu search (TS) as a local improvement procedure enables the H-PSOTS algorithm to avoid local optima and show satisfactory performance. Pure particle swarm optimization (PSO) and H-PSOTS algorithms were applied to three benchmark networks proposed in the literature: the Balerma irrigation network, the ZJ network and the Rural network. The hybrid methodology obtained good results when seeking an optimal solution and revealed high computational performance, making it a new option for the optimal design of real water distribution networks.

A Hybrid Particle Swarm Optimization Approach and its Application to Solving Portfolio Selection Problems

2011 ◽  
Vol 15 (4) ◽  
pp. 473-478
Author(s):  
Shamshul Bahar Yaakob ◽  
◽  
Junzo Watada ◽  
Keyword(s):  
Particle Swarm Optimization ◽  
Portfolio Selection ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Method ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Selection Problems ◽  
Hybrid Particle Swarm Optimization ◽  
Trade Offs

In modern portfolio theory, the basic topic is how to construct a diversified portfolio of financial securities to improve trade-offs between risk and return. The objective of this paper is to apply a heuristic algorithm using Particle Swarm Optimization (PSO) to the portfolio selection problem. PSO makes the search algorithm efficient by combining a local search method through self-experience with the global search method through neighboring experience. PSO attempts to balance the exploration-exploitation tradeoff that achieves efficiency and accuracy of optimization. In this paper, a newly obtained approach is proposed by making simple modifications to the standard PSO: the velocity is controlled and the mutation operator of Genetic Algorithms (GA) is added to solve a stagnation problem. Our adaptation and implementation of the PSO search strategy are applied to portfolio selection. Results of typical applications demonstrate that the Velocity Control Hybrid PSO (VC-HPSO) proposed in this study effectively finds optimum solution to portfolio selection problems. Results also show that our proposed method is a viable approach to portfolio selection.

Path Relinking with Multi-Start Tabu Search for the Quadratic Assignment Problem

2013 ◽  
pp. 52-70
Author(s):  
Tabitha James ◽  
Cesar Rego
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Assignment Problem ◽  
Search Algorithm ◽  
Quadratic Assignment Problem ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Path Relinking ◽  
Quadratic Assignment ◽  
Swarm Optimization

This paper introduces a new path relinking algorithm for the well-known quadratic assignment problem (QAP) in combinatorial optimization. The QAP has attracted considerable attention in research because of its complexity and its applicability to many domains. The algorithm presented in this study employs path relinking as a solution combination method incorporating a multistart tabu search algorithm as an improvement method. The resulting algorithm has interesting similarities and contrasts with particle swarm optimization methods. Computational testing indicates that this algorithm produces results that rival the best QAP algorithms. The authors additionally conduct an analysis disclosing how different strategies prove more or less effective depending on the landscapes of the problems to which they are applied. This analysis lays a foundation for developing more effective future QAP algorithms, both for methods based on path relinking and tabu search, and for hybrids of such methods with related processes found in particle swarm optimization.

Sign in / Sign up

Export Citation Format

Share Document