Hybrid Particle Swarm: Tabu Search Optimization Algorithm for Parameter Estimation

2013 ◽  
Author(s):  
Anish Sebastian ◽  
Marco P. Schoen
Keyword(s):  
Tabu Search ◽  
Hybrid Algorithm ◽  
Search Algorithm ◽  
Single Agent ◽  
Particle Swarm ◽  
Estimation Problem ◽  
Benchmark Problems ◽  
Force Output ◽  
Semg Signal ◽  
Advantages And Disadvantages

A hybrid intelligent algorithm is proposed. The algorithm utilizes a particle swarm and a Tabu search algorithm. Swarm based algorithms and single agent based algorithms each, have distinct advantages and disadvantages. The goal of the presented work is to combine the strengths of the two different algorithms in order to achieve a more effective optimization routine. The developed hybrid algorithm is tailored such that it has the capability to adapt to the given cost function during the optimization process. The proposed algorithm is tested on a set of different benchmark problems. In addition, the hybrid algorithm is utilized for solving the estimation problem encountered for estimating the finger force output given a surface electromyogram (sEMG) signal at the input. This estimation problem is commonly encountered while developing a control system for a prosthetic hand.

scholarly journals Hybridizing Beam Search with Tabu Search for the Irregular Packing Problem

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Yunqing Rao ◽  
Peng Wang ◽  
Qiang Luo
Keyword(s):  
Tabu Search ◽  
Hybrid Algorithm ◽  
Numerical Experiments ◽  
Search Algorithm ◽  
Packing Problem ◽  
Benchmark Problems ◽  
Beam Search ◽  
Hybrid Search

The irregular packing problem involves arranging all the irregular pieces on a plate with the objective of maximizing the use of material. In this article, the layout is formed by the ordered sequence of the irregular pieces which is obtained by a hybrid search algorithm and where the order is decoded by a proposed placement principle. First, a novel no-fit-polygon (NFP) generator is introduced. Then, a placement principle is presented with the new NFP generator. Finally, a search algorithm hybridized with beam search (BS) and tabu search (TS) is proposed to search over the sequence. The numerical experiments with many benchmark problems show that the hybrid algorithm is an applicative and effective approach for solving the irregular packing problem. The hybrid algorithm can produce competitive solutions in less time than many other typical algorithms.

scholarly journals GASAT: A Genetic Local Search Algorithm for the Satisfiability Problem

2006 ◽  
Vol 14 (2) ◽  
pp. 223-253 ◽  
Author(s):  
Frédéric Lardeux ◽  
Frédéric Saubion ◽  
Jin-Kao Hao
Keyword(s):  
Tabu Search ◽  
Local Search ◽  
Hybrid Algorithm ◽  
State Of The Art ◽  
Search Algorithm ◽  
Satisfiability Problem ◽  
Local Search Algorithm ◽  
Overall Performance ◽  
Genetic Local Search ◽  
Search Stage

This paper presents GASAT, a hybrid algorithm for the satisfiability problem (SAT). The main feature of GASAT is that it includes a recombination stage based on a specific crossover and a tabu search stage. We have conducted experiments to evaluate the different components of GASAT and to compare its overall performance with state-of-the-art SAT algorithms. These experiments show that GASAT provides very competitive results.

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 Filter-and-Fan Metaheuristic for the 0-1 Multidimensional Knapsack Problem

2012 ◽  
Vol 3 (4) ◽  
pp. 43-63 ◽  
Author(s):  
Mahdi Khemakhem ◽  
Boukthir Haddar ◽  
Khalil Chebil ◽  
Saïd Hanafi
Keyword(s):  
Tabu Search ◽  
Knapsack Problem ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Benchmark Problems ◽  
Tree Search ◽  
Search Procedure ◽  
Multidimensional Knapsack Problem ◽  
Multidimensional Knapsack ◽  
Adaptive Neighborhood

This paper proposes a new hybrid tree search algorithm to the Multidimensional Knapsack Problem (MKP) that effectively combines tabu search with a dynamic and adaptive neighborhood search procedure. The authors’ heuristic, based on a filter-and-fan (F&F) procedure, uses a Linear Programming-based Heuristic to generate a starting solution to the F&F process. A tabu search procedure is used to try to enhance the best solution value provided by the F&F method that generates compound moves by a strategically truncated form of tree search. They report the first application of the F&F method to the MKP. Experimental results obtained on a wide set of benchmark problems clearly demonstrate the competitiveness of the proposed method compared to the state-of-the-art heuristic methods.

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.

An Integrated Hybrid Algorithm Based on Nature Inspired Evolutionary for Radial Basis Function Neural Network Learning

2016 ◽  
Vol 25 (02) ◽  
pp. 1650004 ◽  
Author(s):  
Zhen-Yao Chen ◽  
R. J. Kuo ◽  
Tung-Lai Hu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Hybrid Algorithm ◽  
Particle Swarm ◽  
Learning Performance ◽  
Benchmark Problems ◽  
Neural Network Learning ◽  
Radial Basis

This paper intends to propose an integrated hybrid algorithm for training radial basis function neural network (RBFNN) learning. The proposed integrated of particle swarm and genetic algorithm based optimization (IPGO) algorithm is composed of two approaches based on particle swarm optimization (PSO) and genetic algorithm (GA) for gathering both their virtues to improve the learning performance of RBFNN. The diversity of individuals results in higher chance to search in the direction of global optimal instead of being confined to local optimal particularly in problem with higher complexity. The IPGO algorithm with PSO-based and GA-based approaches has shown promising results in some benchmark problems with three continuous test functions. After proposing the algorithm for these problems with result providing its outperforming performance, this paper supplements a practical application case for the papaya milk sales forecasting to expound the superiority of the IPGO algorithm. In addition, model evaluation results of the case have showed that the IPGO algorithm outperforms other algorithms and auto-regressive moving average (ARMA) models in terms of forecasting accuracy and execution time.

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