scholarly journals Consistent Neighborhood Search for Combinatorial Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
Michel Vasquez ◽  
Nicolas Zufferey
Keyword(s):  
Local Search ◽  
Optimization Problems ◽  
Search Space ◽  
Partial Solution ◽  
Telecommunication Networks ◽  
Frequency Assignment ◽  
Neighborhood Search ◽  
Great Success ◽  
Heuristic Solution ◽  
Complete Solutions

Many optimization problems (from academia or industry) require the use of a local search to find a satisfying solution in a reasonable amount of time, even if the optimality is not guaranteed. Usually, local search algorithms operate in a search space which contains complete solutions (feasible or not) to the problem. In contrast, in Consistent Neighborhood Search (CNS), after each variable assignment, the conflicting variables are deleted to keep the partial solution feasible, and the search can stop when all the variables have a value. In this paper, we formally propose a new heuristic solution method, CNS, which has a search behavior between exhaustive tree search and local search working with complete solutions. We then discuss, with a unified view, the great success of some existing heuristics, which can however be considered within the CNS framework, in various fields: graph coloring, frequency assignment in telecommunication networks, vehicle fleet management with maintenance constraints, and satellite range scheduling. Moreover, some lessons are given in order to have guidelines for the adaptation of CNS to other problems.

Nelder-Mead Evolutionary Hybrid Algorithms

2011 ◽  
pp. 1191-1196 ◽  
Author(s):  
Sanjoy Das
Keyword(s):  
Evolutionary Algorithms ◽  
Local Search ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Space ◽  
Simplex Algorithm ◽  
Stochastic Methods ◽  
Great Success ◽  
Local Minima ◽  
Swarm Optimization

Real world optimization problems are often too complex to be solved through analytic means. Evolutionary algorithms are a class of algorithms that borrow paradigms from nature to address them. These are stochastic methods of optimization that maintain a population of individual solutions, which correspond to points in the search space of the problem. These algorithms have been immensely popular as they are derivativefree techniques, are not as prone to getting trapped in local minima, and can be tailored specifically to suit any given problem. The performance of evolutionary algorithms can be improved further by adding a local search component to them. The Nelder-Mead simplex algorithm (Nelder & Mead, 1965) is a simple local search algorithm that has been routinely applied to improve the search process in evolutionary algorithms, and such a strategy has met with great success. In this article, we provide an overview of the various strategies that have been adopted to hybridize two wellknown evolutionary algorithms - genetic algorithms (GA) and particle swarm optimization (PSO).

The Hybrid Differential Evolution with Dynamic Scaling Mutation and Wrapper Local Search for Optimization Problems

2013 ◽  
Vol 479-480 ◽  
pp. 989-995
Author(s):  
Chun Liang Lu ◽  
Shih Yuan Chiu ◽  
Chih Hsu Hsu ◽  
Shi Jim Yen
Keyword(s):  
Local Search ◽  
Differential Evolution ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Optimization Problems ◽  
Search Space ◽  
Dynamic Scaling ◽  
Multi Objective ◽  
Wide Range ◽  
Single Objective

In this paper, an improved hybrid Differential Evolution (DE) is proposed to enhance optimization performance by cooperating Dynamic Scaling Mutation (DSM) and Wrapper Local Search (WLS) schemes. When evolution speed is standstill, DSM can improve searching ability to achieve better balance between exploitation and exploration in the search space. Furthermore, WLS can disturb individuals to fine tune the searching range around and then properly find better solutions in the evolution progress. The effective particle encoding representation named Particle Segment Operation-Machine Assignment (PSOMA) that we previously published is also applied to always produce feasible candidate solutions for hybrid DE model to solve the Flexible Job-Shop Scheduling Problem (FJSP). To test the performance of the proposed hybrid method, the experiments contain five frequently used CEC 2005 numerical functions and three representative FJSP benchmarks for single-objective and multi-objective optimization verifications, respectively. Compare the proposed method with the other related published algorithms, the simulation results indicate that our proposed method exhibits better performance for solving most the test functions for single-objective problems. In addition, the wide range of Pareto-optimal solutions and the more Gantt chart diversities can be obtained for the multi-objective FJSP in practical decision-making considerations.

scholarly journals Three Local Search Meta-Heuristics for the Minimum Interference Frequency Assignment Problem (MI-FAP) in Cellular Networks

2019 ◽  
Vol 10 (3) ◽  
pp. 134-150
Author(s):  
Yasmine Lahsinat ◽  
Dalila Boughaci ◽  
Belaid Benhamou
Keyword(s):  
Local Search ◽  
Cellular Networks ◽  
Assignment Problem ◽  
Frequency Assignment ◽  
Neighborhood Search ◽  
Stochastic Local Search ◽  
Frequency Assignment Problem ◽  
Solution Quality ◽  
Interference Frequency ◽  
Minimum Interference

The minimum interference frequency assignment problem (MI-FAP) plays an important role in cellular networks. MI-FAP is the problem of finding an assignment of a small number of frequencies to a large number of transceivers (TRXs) that minimizes the interferences level. The MI-FAP is known to be NP-Hard, thus it cannot be solved in polynomial time. To remedy this, researchers usually use meta-heuristic techniques to find an approximate solution in reasonable time. Here, the authors propose three meta-heuristics for the MI-FAP: a variable neighborhood search (VNS) and a stochastic local search (SLS) that are combined to obtain a third and a new one, which is called VNS-SLS. The SLS method is incorporated into the VNS process as a subroutine in order to enhance the solution quality. All three proposed methods are evaluated on some well-known datasets to measure their performance. The empirical experiments show that the proposed method VNS-SLS succeeds in finding good results compared to both VNS and SLS confirming a good balance between intensification and diversification.

scholarly journals Improved Particle Swarm Optimization with a Collective Local Unimodal Search for Continuous Optimization Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Martins Akugbe Arasomwan ◽  
Aderemi Oluyinka Adewumi
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Continuous Optimization ◽  
Search Space ◽  
Benchmark Problems ◽  
Search Technique ◽  
Swarm Optimization ◽  
Particle Position

A new local search technique is proposed and used to improve the performance of particle swarm optimization algorithms by addressing the problem of premature convergence. In the proposed local search technique, a potential particle position in the solution search space is collectively constructed by a number of randomly selected particles in the swarm. The number of times the selection is made varies with the dimension of the optimization problem and each selected particle donates the value in the location of its randomly selected dimension from its personal best. After constructing the potential particle position, some local search is done around its neighbourhood in comparison with the current swarm global best position. It is then used to replace the global best particle position if it is found to be better; otherwise no replacement is made. Using some well-studied benchmark problems with low and high dimensions, numerical simulations were used to validate the performance of the improved algorithms. Comparisons were made with four different PSO variants, two of the variants implement different local search technique while the other two do not. Results show that the improved algorithms could obtain better quality solution while demonstrating better convergence velocity and precision, stability, robustness, and global-local search ability than the competing variants.

scholarly journals A novel population-based local search for nurse rostering problem

2021 ◽  
Vol 11 (1) ◽  
pp. 471
Author(s):  
Anmar Abuhamdah ◽  
Wadii Boulila ◽  
Ghaith M. Jaradat ◽  
Anas M. Quteishat ◽  
Mutasem K. Alsmadi ◽  
...  
Keyword(s):  
Local Search ◽  
Optimization Problems ◽  
Hybrid Methods ◽  
Parameter Tuning ◽  
Search Space ◽  
Population Based ◽  
Nurse Rostering ◽  
Hybrid Approaches ◽  
University Course Timetabling ◽  
Nurse Rostering Problem

Population-based approaches regularly are better than single based (local search) approaches in exploring the search space. However, the drawback of population-based approaches is in exploiting the search space. Several hybrid approaches have proven their efficiency through different domains of optimization problems by incorporating and integrating the strength of population and local search approaches. Meanwhile, hybrid methods have a drawback of increasing the parameter tuning. Recently, population-based local search was proposed for a university course-timetabling problem with fewer parameters than existing approaches, the proposed approach proves its effectiveness. The proposed approach employs two operators to intensify and diversify the search space. The first operator is applied to a single solution, while the second is applied for all solutions. This paper aims to investigate the performance of population-based local search for the nurse rostering problem. The INRC2010 database with a dataset composed of 69 instances is used to test the performance of PB-LS. A comparison was made between the performance of PB-LS and other existing approaches in the literature. Results show good performances of proposed approach compared to other approaches, where population-based local search provided best results in 55 cases over 69 instances used in experiments.

scholarly journals Hybridization of Adaptive Differential Evolution with an Expensive Local Search Method

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Rashida Adeeb Khanum ◽  
Muhammad Asif Jan ◽  
Nasser Mansoor Tairan ◽  
Wali Khan Mashwani
Keyword(s):  
Global Optimization ◽  
Local Search ◽  
Differential Evolution ◽  
Large Scale ◽  
Optimization Problems ◽  
Neighborhood Search ◽  
Test Problems ◽  
Adaptive Differential Evolution ◽  
External Archive ◽  
Search Capability

Differential evolution (DE) is an effective and efficient heuristic for global optimization problems. However, it faces difficulty in exploiting the local region around the approximate solution. To handle this issue, local search (LS) techniques could be hybridized with DE to improve its local search capability. In this work, we hybridize an updated version of DE, adaptive differential evolution with optional external archive (JADE) with an expensive LS method, Broydon-Fletcher-Goldfarb-Shano (BFGS) for solving continuous unconstrained global optimization problems. The new hybrid algorithm is denoted by DEELS. To validate the performance of DEELS, we carried out extensive experiments on well known test problems suits, CEC2005 and CEC2010. The experimental results, in terms of function error values, success rate, and some other statistics, are compared with some of the state-of-the-art algorithms, self-adaptive control parameters in differential evolution (jDE), sequential DE enhanced by neighborhood search for large-scale global optimization (SDENS), and differential ant-stigmergy algorithm (DASA). These comparisons reveal that DEELS outperforms jDE and SDENS except DASA on the majority of test instances.

Approximation based curvilinear local search for optimization problems

2016 ◽  
Vol 33 (2) ◽  
Author(s):  
Fatih Yaman ◽  
Asim Egemen Yilmaz ◽  
Kemal Leblebicioğlu
Keyword(s):  
Local Search ◽  
Design Methodology ◽  
Reference Data ◽  
Optimization Problems ◽  
Interpolation Method ◽  
Computational Cost ◽  
Search Space ◽  
Approximation Technique ◽  
Search Mechanism ◽  
Alpha Beta

Purpose At this work, we propose a local approximation based search method to optimize any function. For this purpose, an approximation method is combined with an estimation filter, and a new local search mechanism is constituted. Design/methodology/approach RBF network is very efficient interpolation method especially if we have sufficient reference data. Here, reference data refers to the exact value of the objective function at some points. Using this capability of RBFs, we can approximately inspect the vicinity each point in search space. Meanwhile, in order to obtain a smooth, rapid and better trajectory toward the global optimum, the alpha-beta filter can be integrated to this mechanism. For better description and visualization, the operations are defined in 2-dimensional search space; but the outlined procedure can be extended to higher dimensions without loss of generality. Findings When compared with our previous studies using conventional heuristic methods, approximation based curvilinear local search mechanism provide better minimization performance for almost all benchmark functions. Moreover computational cost of this method too less than heuristics. The number of iteration down to at least 1/10 compared to conventional heuristic algorithm. Additionally, the solution accuracy is very improved for majority of the test cases. Originality/value This paper proposes a new search approach to solve optimization problems with less cost. For this purpose, a new local curvilinear search mechanism is built using RBF based approximation technique and alpha-beta estimation filter.

scholarly journals Improved quantum-behaved particle swarm optimization with local search strategy

2016 ◽  
Vol 11 (1) ◽  
pp. 3-12 ◽  
Author(s):  
Maolong Xi ◽  
Xiaojun Wu ◽  
Xinyi Sheng ◽  
Jun Sun ◽  
Wenbo Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Search Strategy ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Performance Comparison ◽  
Swarm Optimization ◽  
Fitness Value ◽  
Local Search Strategy

Quantum-behaved particle swarm optimization, which was motivated by analysis of particle swarm optimization and quantum system, has shown compared performance in finding the optimal solutions for many optimization problems to other evolutionary algorithms. To address the problem of premature, a local search strategy is proposed to improve the performance of quantum-behaved particle swarm optimization. In proposed local search strategy, a super particle is presented which is a collection body of randomly selected particles’ dimension information in the swarm. The selected probability of particles in swarm is different and determined by their fitness values. To minimization problems, the fitness value of one particle is smaller; the selected probability is more and will contribute more information in constructing the super particle. In addition, in order to investigate the influence on algorithm performance with different local search space, four methods of computing the local search radius are applied in local search strategy and propose four variants of local search quantum-behaved particle swarm optimization. Empirical studies on a suite of well-known benchmark functions are undertaken in order to make an overall performance comparison among the proposed methods and other quantum-behaved particle swarm optimization. The simulation results show that the proposed quantum-behaved particle swarm optimization variants have better advantages over the original quantum-behaved particle swarm optimization.

Reverse guidance butterfly optimization algorithm integrated with information cross-sharing

2021 ◽  
pp. 1-22
Author(s):  
Jun Luo ◽  
Qin Tian ◽  
Meng Xu
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Search Space ◽  
Experimental Results ◽  
Neighborhood Search ◽  
Weight Factor ◽  
Exploration And Exploitation ◽  
Local Optima ◽  
Slow Convergence ◽  
Engineering Problems

Aiming at the disadvantages of slow convergence and the premature phenomenon of the butterfly optimization algorithm (BOA), this paper proposes a modified BOA (MBOA) called reverse guidance butterfly optimization algorithm integrated with information cross-sharing. First, the quasi-opposition concept is employed in the global search phase that lacks local exploitation capabilities to broaden the search space. Second, the neighborhood search weight factor is added in the local search stage to balance exploration and exploitation. Finally, the information cross-sharing mechanism is introduced to enhance the ability of the algorithm to jump out of the local optima. The proposed MBOA is tested in fourteen benchmark functions and three constrained engineering problems. The series of experimental results indicate that MBOA shows better performance in terms of convergence speed, convergence accuracy, stability as well as robustness.

scholarly journals Gaussian variable neighborhood search for the file transfer scheduling problem

2016 ◽  
Vol 26 (2) ◽  
pp. 173-188
Author(s):  
Zorica Drazic
Keyword(s):  
Local Search ◽  
Variable Neighborhood Search ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Neighborhood Search ◽  
Discrete Problem ◽  
Scheduling Problem ◽  
File Transfer ◽  
Large Size ◽  
Gaussian Variable

This paper presents new modifications of Variable Neighborhood Search approach for solving the file transfer scheduling problem. To obtain better solutions in a small neighborhood of a current solution, we implement two new local search procedures. As Gaussian Variable Neighborhood Search showed promising results when solving continuous optimization problems, its implementation in solving the discrete file transfer scheduling problem is also presented. In order to apply this continuous optimization method to solve the discrete problem, mapping of uncountable set of feasible solutions into a finite set is performed. Both local search modifications gave better results for the large size instances, as well as better average performance for medium and large size instances. One local search modification achieved significant acceleration of the algorithm. The numerical experiments showed that the results obtained by Gaussian modifications are comparable with the results obtained by standard VNS based algorithms, developed for combinatorial optimization. In some cases Gaussian modifications gave even better results.

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