Particle swarm optimization based on the concept of tabu search

2007 ◽  
Author(s):  
Shinichi Nakano ◽  
Atsushi Ishigame ◽  
Keiichiro Yasuda
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Particle Swarm ◽  
Swarm Optimization

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.

A Condensed Hybrid Optimization Algorithm Using Enhanced Continuous Tabu Search and Particle Swarm Optimization

2009 ◽  
Author(s):  
Cheng-Hung Chen ◽  
Marco P. Schoen ◽  
Ken W. Bosworth
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Particle Swarm ◽  
Search Space ◽  
Hybrid Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Search Spaces ◽  
Hybrid Optimization Algorithm ◽  
Promising Area

A novel Condensed Hybrid Optimization (CHO) algorithm using Enhanced Continuous Tabu Search (ECTS) and Particle Swarm Optimization (PSO) is proposed. The proposed CHO algorithm combines the respective strengths of ECTS and PSO. The ECTS is a modified Tabu Search (TS), which has good search capabilities on large search spaces. In this study, ECTS is utilized to define smaller search spaces, which are used in a second stage by the basic PSO to find the respective local optimum. The ECTS covers the global search space by using a TS concept called diversification and then selects the most promising areas in the search space. Once the promising regions in the search space are defined, the proposed CHO algorithm employs another TS concept called intensification in order to search the promising area thoroughly. The proposed CHO algorithm is tested with the multi-dimensional Hyperbolic and Rosenbrock problems. Compared to other four algorithms, the simulations results indicate that the accuracy and effectiveness of the proposed CHO algorithm.

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.

scholarly journals A Discrete Particle Swarm Optimization to Solve the Put-Away Routing Problem in Distribution Centres

Computation ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 99
Author(s):  
Rodrigo Andrés Gómez-Montoya ◽  
Jose Alejandro Cano ◽  
Pablo Cortés ◽  
Fernando Salazar
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Material Handling ◽  
Particle Swarm ◽  
Homogeneous Material ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Discrete Particle ◽  
Material Handling Equipment

Put-away operations typically consist of moving products from depots to allocated storage locations using either operators or Material Handling Equipment (MHE), accounting for important operative costs in warehouses and impacting operations efficiency. Therefore, this paper aims to formulate and solve a Put-away Routing Problem (PRP) in distribution centres (DCs). This PRP formulation represents a novel approach due to the consideration of a fleet of homogeneous Material Handling Equipment (MHE), heterogeneous products linked to a put-away list size, depot location and multi-parallel aisles in a distribution centre. It should be noted that the slotting problem, rather than the PRP, has usually been studied in the literature, whereas the PRP is addressed in this paper. The PRP is solved using a discrete particle swarm optimization (PSO) algorithm that is compared to tabu search approaches (Classical Tabu Search (CTS), Tabu Search (TS) 2-Opt) and an empirical rule. As a result, it was found that a discrete PSO generates the best solutions, as the time savings range from 2 to 13% relative to CTS and TS 2-Opt for different combinations of factor levels evaluated in the experimentation.

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