scholarly journals Adaptive Self-Scaling Brain-Storm Optimization via a Chaotic Search Mechanism

Algorithms ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 239
Author(s):  
Zhenyu Song ◽  
Xuemei Yan ◽  
Lvxing Zhao ◽  
Luyi Fan ◽  
Cheng Tang ◽  
...  
Keyword(s):  
Local Search ◽  
Local Development ◽  
Search Space ◽  
Population Based ◽  
Stability And Convergence ◽  
Search Performance ◽  
Local Optima ◽  
Brain Storm Optimization ◽  
Search Mechanism ◽  
Benchmark Test Functions

Brain-storm optimization (BSO), which is a population-based optimization algorithm, exhibits a poor search performance, premature convergence, and a high probability of falling into local optima. To address these problems, we developed the adaptive mechanism-based BSO (ABSO) algorithm based on the chaotic local search in this study. The adjustment of the search space using the local search method based on an adaptive self-scaling mechanism balances the global search and local development performance of the ABSO algorithm, effectively preventing the algorithm from falling into local optima and improving its convergence accuracy. To verify the stability and effectiveness of the proposed ABSO algorithm, the performance was tested using 29 benchmark test functions, and the mean and standard deviation were compared with those of five other optimization algorithms. The results showed that ABSO outperforms the other algorithms in terms of stability and convergence accuracy. In addition, the performance of ABSO was further verified through a nonparametric statistical test.

scholarly journals VIBRANT SEARCH MECHANISM FOR NUMERICAL OPTIMIZATION FUNCTIONS

2018 ◽  
Author(s):  
Moh’d Khaled Yousef Shambour
Keyword(s):  
Convergence Rates ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Search Performance ◽  
Search Spaces ◽  
Search Mechanism ◽  
Heuristic Technique

Recently, various variants of evolutionary algorithms have been offered to optimize the exploration and exploitation abilities of the search mechanism. Some of these variants still suffer from slow convergence rates around the optimal solution. In this paper, a novel heuristic technique is introduced to enhance the search capabilities of an algorithm, focusing on certain search spaces during evolution process. Then, employing a heuristic search mechanism that scans an entire space before determining the desired segment of that search space. The proposed method randomly updates the desired segment by monitoring the algorithm search performance levels on different search space divisions. The effectiveness of the proposed technique is assessed through harmony search algorithm (HSA). The performance of this mechanism is examined with several types of benchmark optimization functions, and the results are compared with those of the classic version and two variants of HSA. The experimental results demonstrate that the proposed technique achieves the lowest values (best results) in 80% of the non-shifted functions, whereas only 33.3% of total experimental cases are achieved within the shifted functions in a total of 30 problem dimensions. In 100 problem dimensions, 100% and 25% of the best results are reported for non-shifted and shifted functions, respectively. The results reveal that the proposed technique is able to orient the search mechanism toward desired segments of search space, which therefore significantly improves the overall search performance of HSA, especially for non-shifted optimization functions.   

scholarly journals Cooperative Quantum-Behaved Particle Swarm Optimization with Dynamic Varying Search Areas and Lévy Flight Disturbance

2014 ◽  
Vol 2014 ◽  
pp. 1-11
Author(s):  
Desheng Li
Keyword(s):  
Particle Swarm Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Particle Swarm ◽  
Search Space ◽  
Swarm Optimization ◽  
Local Optima ◽  
Stochastic Disturbance ◽  
Novel Variant ◽  
Benchmark Test Functions

This paper proposes a novel variant of cooperative quantum-behaved particle swarm optimization (CQPSO) algorithm with two mechanisms to reduce the search space and avoid the stagnation, called CQPSO-DVSA-LFD. One mechanism is called Dynamic Varying Search Area (DVSA), which takes charge of limiting the ranges of particles’ activity into a reduced area. On the other hand, in order to escape the local optima, Lévy flights are used to generate the stochastic disturbance in the movement of particles. To test the performance of CQPSO-DVSA-LFD, numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on both benchmark test functions and the combinatorial optimization issue, that is, the job-shop scheduling problem.

scholarly journals Local Search-based Non-dominated Sorting Genetic Algorithm for Optimal Design of Multimachine Power System Stabilizers

2021 ◽  
Vol 11 (3) ◽  
pp. 7283-7289
Author(s):  
F. A. Alshammari ◽  
G. A. Alshammari ◽  
T. Guesmi ◽  
A. A. Alzamil ◽  
B. M. Alshammari ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Power System ◽  
Local Search ◽  
Operating Conditions ◽  
Power System Stabilizers ◽  
Local Optima ◽  
Electromechanical Oscillations ◽  
Search Mechanism ◽  
Wide Range ◽  
Metaheuristic Techniques

This study presents a metaheuristic method for the optimum design of multimachine Power System Stabilizers (PSSs). In the proposed method, referred to as Local Search-based Non-dominated Sorting Genetic Algorithm (LSNSGA), a local search mechanism is incorporated at the end of the second version of the non-dominated sorting genetic algorithm in order to improve its convergence rate and avoid the convergence to local optima. The parameters of PSSs are tuned using LSNSGA over a wide range of operating conditions, in order to provide the best damping of critical electromechanical oscillations. Eigenvalue-based objective functions are employed in the PSS design process. Simulation results based on eigenvalue analysis and nonlinear time-domain simulation proved that the proposed controller provided competitive results compared to other metaheuristic techniques.

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 An improved hybrid Aquila Optimizer and Harris Hawks Optimization for global optimization

2021 ◽  
Vol 18 (6) ◽  
pp. 7076-7109
Author(s):  
Shuang Wang ◽  
◽  
Heming Jia ◽  
Qingxin Liu ◽  
Rong Zheng ◽  
...  
Keyword(s):  
Global Optimization ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Space ◽  
Search Performance ◽  
Exploration And Exploitation ◽  
Design Problems ◽  
Local Optima ◽  
Opposition Based Learning ◽  
Engineering Design Problems

<abstract> <p>This paper introduces an improved hybrid Aquila Optimizer (AO) and Harris Hawks Optimization (HHO) algorithm, namely IHAOHHO, to enhance the searching performance for global optimization problems. In the IHAOHHO, valuable exploration and exploitation capabilities of AO and HHO are retained firstly, and then representative-based hunting (RH) and opposition-based learning (OBL) strategies are added in the exploration and exploitation phases to effectively improve the diversity of search space and local optima avoidance capability of the algorithm, respectively. To verify the optimization performance and the practicability, the proposed algorithm is comprehensively analyzed on standard and CEC2017 benchmark functions and three engineering design problems. The experimental results show that the proposed IHAOHHO has more superior global search performance and faster convergence speed compared to the basic AO and HHO and selected state-of-the-art meta-heuristic algorithms.</p> </abstract>

A New Hybrid Binary-Real Coded Cuckoo Search and Tabu Search Algorithm for Solving the Unit-Commitment Problem

2021 ◽  
Vol 10 (2) ◽  
pp. 104-119
Author(s):  
Amel Terki ◽  
Hamid Boubertakh
Keyword(s):  
Tabu Search ◽  
Local Search ◽  
Unit Commitment ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Optimization Techniques ◽  
Optimization Approach ◽  
Unit Commitment Problem ◽  
Local Optima

This paper proposes a new intelligent optimization approach to deal with the unit commitment (UC) problem by finding the optimal on/off states strategy of the units under the system constraints. The proposed method is a hybridization of the cuckoo search (CS) and the tabu search (TS) optimization techniques. The former is distinguished by its efficient global exploration mechanism, namely the levy flights, and the latter is a successful local search method. For this sake, a binary code is used for the status of units in the scheduled time horizon, and a real code is used to determine the generated power by the committed units. The proposed hybrid CS and TS (CS-TS) algorithm is used to solve the UC problem such that the CS guarantees the exploration of the whole search space, while the TS algorithm deals with the local search in order to avoid the premature convergence and prevent from trapping into local optima. The proposed method is applied to the IEEE standard systems of different scales ranging from 10 to 100 units. The results show clearly that the proposed method gives better quality solutions than the existing methods.

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 Integrating Iterative Crossover Capability in Orthogonal Neighborhoods for Scheduling Resource-Constrained Projects

2013 ◽  
Vol 21 (2) ◽  
pp. 341-360 ◽  
Author(s):  
Reza Zamani
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Project Scheduling ◽  
Search Space ◽  
Search Method ◽  
Evolutionary Search ◽  
Resource Constrained ◽  
Search Mechanism ◽  
Benchmark Instances ◽  
Project Scheduling Problem

An effective hybrid evolutionary search method is presented which integrates a genetic algorithm with a local search. Whereas its genetic algorithm improves the solutions obtained by its local search, its local search component utilizes a synergy between two neighborhood schemes in diversifying the pool used by the genetic algorithm. Through the integration of these two searches, the crossover operators further enhance the solutions that are initially local optimal for both neighborhood schemes; and the employed local search provides fresh solutions for the pool whenever needed. The joint endeavor of its local search mechanism and its genetic algorithm component has made the method both robust and effective. The local search component examines unvisited regions of search space and consequently diversifies the search; and the genetic algorithm component recombines essential pieces of information existing in several high-quality solutions and intensifies the search. It is through striking such a balance between diversification and intensification that the method exploits the structure of search space and produces superb solutions. The method has been implemented as a procedure for the resource-constrained project scheduling problem. The computational experiments on 2,040 benchmark instances indicate that the procedure is very effective.

scholarly journals Data-driven optimization algorithm for economic operation of microgrid based on dynamic Kriging model and two-stage search strategy

2021 ◽  
Author(s):  
Chaodong Fan ◽  
Hou Bo ◽  
Xilong Qu ◽  
Leyi Xiao ◽  
Fanyong Cheng
Keyword(s):  
Local Search ◽  
Optimization Algorithm ◽  
Fitness Landscape ◽  
Kriging Model ◽  
Operating Modes ◽  
Economic Operation ◽  
Search Mechanism ◽  
Comparison Algorithms ◽  
Benchmark Test Functions ◽  
Dynamic Kriging

Abstract The economic operation optimization of microgrid is an important research topic in the power system. Therefore, this paper proposes a surrogate model particle swarm optimization algorithm based on the global-local search mechanism. Firstly, aiming at the problem that the statistical information of Kriging model is difficult to guarantee the prediction accuracy, the dynamic transformation is carried out to enhance the robustness of the model; secondly, the global-local search mechanism is introduced to make the algorithm fully explore the fitness landscape near the Kriging model after quickly locating the current optimal particle position, so as to achieve the balance of convergence quality and convergence efficiency. The proposed method has been tested on numerous benchmark test functions from two test suites, and the results show that the proposed algorithm has more advantages than other comparison algorithms in optimization accuracy. Finally, simulations are carried out in two operating modes of microgrid islanding and grid-connected, which has verified the effectiveness of the proposed method.

scholarly journals Relaxation and Clustering in a Local Search Framework: Application to Linear Placement

VLSI Design ◽  
2002 ◽  
Vol 14 (2) ◽  
pp. 143-154 ◽  
Author(s):  
Sung-Woo Hur ◽  
John Lillis
Keyword(s):  
Local Search ◽  
Visualization Tool ◽  
Dynamic Clustering ◽  
Placement Problem ◽  
Design Problems ◽  
Local Optima ◽  
Constraint Relaxation ◽  
Search Mechanism ◽  
The Relationship

This paper presents two primary results relevant to physical design problems in CAD/VLSI through a case study of the linear placement problem. First a local search mechanism which incorporates a sophisticated neighborhood operator based on constraint relaxation is proposed. The strategy exhibits many of the desirable features of analytical placement while retaining the flexibility and non-determinism of local search. The second and orthogonal contribution is in netlist clustering. We characterize local optima in the linear placement problem through a simple visualization tool—the displacement graph. This characterization reveals the relationship between clusters and local optima and motivates a dynamic clustering scheme designed specifically for escaping such local optima. Promising experimental results are reported.

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