Local search algorithms based on benchmark test functions problem

<p>Optimization process is normally implemented to solve several objectives in the form of single or multi-objectives modes. Some traditional optimization techniques are computationally burdensome which required exhaustive computational times. Thus, many studies have invented new optimization techniques to address the issues. To realize the effectiveness of the proposed techniques, implementation on several benchmark functions is crucial. In solving benchmark test functions, local search algorithms have been rigorously examined and employed to diverse tasks. This paper highlights different algorithms implemented to solve several problems. The capacity of local search algorithms in the resolution of engineering optimization problem including benchmark test functions is reviewed. The use of local search algorithms, mainly Simulated Annealing (SA) and Great Deluge (GD) according to solve different problems is presented. Improvements and hybridization of the local search and global search algorithms are also reviewed in this paper. Consequently, benchmark test functions are proposed to those involved in local search algorithm.</p>

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Efficient Multi-Start Strategies for Local Search Algorithms

Journal of Artificial Intelligence Research ◽

10.1613/jair.3313 ◽

2011 ◽

Vol 41 ◽

pp. 407-444 ◽

Cited By ~ 20

Author(s):

A. György ◽

L. Kocsis

Keyword(s):

Local Search ◽

Optimization Problems ◽

Search Algorithm ◽

Target Function ◽

Search Algorithms ◽

Local Optimum ◽

Local Search Algorithm ◽

Local Search Algorithms ◽

Unknown Constant ◽

Lipschitz Optimization

Local search algorithms applied to optimization problems often suffer from getting trapped in a local optimum. The common solution for this deficiency is to restart the algorithm when no progress is observed. Alternatively, one can start multiple instances of a local search algorithm, and allocate computational resources (in particular, processing time) to the instances depending on their behavior. Hence, a multi-start strategy has to decide (dynamically) when to allocate additional resources to a particular instance and when to start new instances. In this paper we propose multi-start strategies motivated by works on multi-armed bandit problems and Lipschitz optimization with an unknown constant. The strategies continuously estimate the potential performance of each algorithm instance by supposing a convergence rate of the local search algorithm up to an unknown constant, and in every phase allocate resources to those instances that could converge to the optimum for a particular range of the constant. Asymptotic bounds are given on the performance of the strategies. In particular, we prove that at most a quadratic increase in the number of times the target function is evaluated is needed to achieve the performance of a local search algorithm started from the attraction region of the optimum. Experiments are provided using SPSA (Simultaneous Perturbation Stochastic Approximation) and k-means as local search algorithms, and the results indicate that the proposed strategies work well in practice, and, in all cases studied, need only logarithmically more evaluations of the target function as opposed to the theoretically suggested quadratic increase.

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Circumspect descent prevails in solving random constraint satisfaction problems

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.0712263105 ◽

2008 ◽

Vol 105 (40) ◽

pp. 15253-15257 ◽

Cited By ~ 30

Author(s):

Mikko Alava ◽

John Ardelius ◽

Erik Aurell ◽

Petteri Kaski ◽

Supriya Krishnamurthy ◽

...

Keyword(s):

Local Search ◽

Linear Time ◽

Search Algorithm ◽

Solution Space ◽

Search Algorithms ◽

Constraint Satisfaction Problems ◽

Problem Instance ◽

Stochastic Local Search ◽

Local Search Algorithms ◽

Local Energy Minimum

We study the performance of stochastic local search algorithms for random instances of the K-satisfiability (K-SAT) problem. We present a stochastic local search algorithm, ChainSAT, which moves in the energy landscape of a problem instance by never going upwards in energy. ChainSAT is a focused algorithm in the sense that it focuses on variables occurring in unsatisfied clauses. We show by extensive numerical investigations that ChainSAT and other focused algorithms solve large K-SAT instances almost surely in linear time, up to high clause-to-variable ratios α; for example, for K = 4 we observe linear-time performance well beyond the recently postulated clustering and condensation transitions in the solution space. The performance of ChainSAT is a surprise given that by design the algorithm gets trapped into the first local energy minimum it encounters, yet no such minima are encountered. We also study the geometry of the solution space as accessed by stochastic local search algorithms.

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Local Search with Dynamic-Threshold Configuration Checking and Incremental Neighborhood Updating for Maximum k-plex Problem

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i03.5613 ◽

2020 ◽

Vol 34 (03) ◽

pp. 2343-2350 ◽

Cited By ~ 1

Author(s):

Peilin Chen ◽

Hai Wan ◽

Shaowei Cai ◽

Jia Li ◽

Haicheng Chen

Keyword(s):

Local Search ◽

Search Algorithm ◽

Promising Result ◽

Dynamic Threshold ◽

Local Search Algorithm ◽

Local Search Algorithms ◽

Worst Case ◽

Massive Graphs ◽

Configuration Checking ◽

Novel Strategy

The Maximum k-plex Problem is an important combinatorial optimization problem with increasingly wide applications. In this paper, we propose a novel strategy, named Dynamic-threshold Configuration Checking (DCC), to reduce the cycling problem of local search. Due to the complicated neighborhood relations, all the previous local search algorithms for this problem spend a large amount of time in identifying feasible neighbors in each step. To further improve the performance on dense and challenging instances, we propose Double-attributes Incremental Neighborhood Updating (DINU) scheme which reduces the worst-case time complexity per iteration from O(|V|⋅ΔG) to O(k · Δ‾G). Based on DCC strategy and DINU scheme, we develop a local search algorithm named DCCplex. According to the experiment result, DCCplex shows promising result on DIMACS and BHOSLIB benchmark as well as real-world massive graphs. Especially, DCCplex updates the lower bound of the maximum k-plex for most dense and challenging instances.

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An Improved Novel Global Harmony Search Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.644-650.2169 ◽

2014 ◽

Vol 644-650 ◽

pp. 2169-2172

Author(s):

Zhi Kong ◽

Guo Dong Zhang ◽

Li Fu Wang

Keyword(s):

Convergence Rate ◽

Optimization Problems ◽

Search Algorithm ◽

Harmony Search ◽

Global Optimum ◽

Harmony Search Algorithm ◽

Test Functions ◽

Benchmark Test ◽

Novel Method ◽

Benchmark Test Functions

This paper develops an improved novel global harmony search (INGHS) algorithm for solving optimization problems. INGHS employs a novel method for generating new solution vectors that enhances accuracy and convergence rate of novel global harmony search (NGHS) algorithm. Simulations for five benchmark test functions show that INGHS possesses better ability to find the global optimum than that of harmony search (HS) algorithm. Compared with NGHS and HS, INGHS is better in terms of robustness and efficiency.

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A Variable Neighborhood Walksat-Based Algorithm for MAX-SAT Problems

The Scientific World JOURNAL ◽

10.1155/2014/798323 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Noureddine Bouhmala

Keyword(s):

Local Search ◽

Optimization Problems ◽

Search Algorithm ◽

Search Algorithms ◽

Boolean Formula ◽

Local Search Algorithms ◽

Neighborhood Structure ◽

Np Complete ◽

Max Sat ◽

Almost All

The simplicity of the maximum satisfiability problem (MAX-SAT) combined with its applicability in many areas of artificial intelligence and computing science made it one of the fundamental optimization problems. This NP-complete problem refers to the task of finding a variable assignment that satisfies the maximum number of clauses (or the sum of weights of satisfied clauses) in a Boolean formula. The Walksat algorithm is considered to be the main skeleton underlying almost all local search algorithms for MAX-SAT. Most local search algorithms including Walksat rely on the 1-flip neighborhood structure. This paper introduces a variable neighborhood walksat-based algorithm. The neighborhood structure can be combined easily using any local search algorithm. Its effectiveness is compared with existing algorithms using 1-flip neighborhood structure and solvers such as CCLS and Optimax from the eighth MAX-SAT evaluation.

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Highly Precise Modified Blue Whale Method Framed by Blending Bat and Local Search Algorithm for the Optimality of Image Fusion Algorithm

Journal of Soft Computing Paradigm - September 2019 ◽

10.36548/jscp.2020.4.001 ◽

2020 ◽

Vol 2 (4) ◽

pp. 195-208

Author(s):

Sayantan Dutta ◽

Ayan Banerjee

Keyword(s):

Local Search ◽

Image Fusion ◽

Optimization Algorithm ◽

Search Algorithm ◽

Optimization Techniques ◽

Whale Optimization Algorithm ◽

Local Search Algorithm ◽

Fusion Algorithm ◽

Whale Optimization ◽

Nature Inspired Algorithm

Image fusion has gained huge popularity in the field of medical and satellite imaging for image analysis. The lack of usages of image fusion is due to a deficiency of suitable optimization techniques and dedicated hardware. In recent days WOA (whale optimization algorithm) is gaining popularity. Like another straightforward nature-inspired algorithm, WOA has some problems in its searching process. In this paper, we have tried to improve the WOA algorithm by modifying the WOA algorithm. This MWOA (modified whale optimization algorithm) algorithm is amalgamed with LSA (local search algorithm) and BA (bat algorithm). The LSA algorithm helps the system to be faster, and BA algorithm helps to increase the accuracy of the system. This optimization algorithm is checked using MATLAB R2018b. Simulated using ModelSim, and the synthesizing is done using Xilinx Vivado 18.2 synthesis tool. The outcome of the simulation result and the synthesis result outshine other metaheuristic optimization algorithms.

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Socially Motivated Partial Cooperation in Multi-agent Local Search

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/81 ◽

2018 ◽

Author(s):

Tal Ze'evi ◽

Roie Zivan ◽

Omer Lev

Keyword(s):

Local Search ◽

Optimization Problems ◽

Search Algorithm ◽

Search Algorithms ◽

Scheduling Problems ◽

Multi Agent Systems ◽

Local Search Algorithms ◽

Partial Cooperation ◽

Multi Agent ◽

Socially Motivated

Partial Cooperation is a paradigm and a corresponding model, proposed to represent multi-agent systems in which agents are willing to cooperate to achieve a global goal, as long as some minimal threshold on their personal utility is satisfied. Distributed local search algorithms were proposed in order to solve asymmetric distributed constraint optimization problems (ADCOPs) in which agents are partially cooperative. We contribute by: 1) extending the partial cooperative model to allow it to represent dynamic cooperation intentions, affected by changes in agents’ wealth, in accordance with social studies literature. 2) proposing a novel local search algorithm in which agents receive indications of others’ preferences on their actions and thus, can perform actions that are socially beneficial. Our empirical study reveals the advantage of the proposed algorithm in multiple benchmarks. Specifically, on realistic meeting scheduling problems it overcomes limitations of standard local search algorithms.

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