Optimization of Loneys Solenoid Design Using a Dynamic Search Based Technique

2021 ◽  
Vol 36 (1) ◽  
pp. 35-40
Author(s):  
Shanshan Tu ◽  
Obaid Rehman ◽  
Sadaqat Rehman ◽  
Shafi Khan ◽  
Muhammad Waqas ◽  
...  
Keyword(s):  
Particle Swarm ◽  
Global Optimum ◽  
Test Functions ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Local Optima ◽  
Good Balance ◽  
Dynamic Search ◽  
Optimum Solution ◽  
Global Optimum Solution

Particle swarm optimizer is one of the searched based stochastic technique that has a weakness of being trapped into local optima. Thus, to tradeoff between the local and global searches and to avoid premature convergence in PSO, a new dynamic quantum-based particle swarm optimization (DQPSO) method is proposed in this work. In the proposed method a beta probability distribution technique is used to mutate the particle with the global best position of the swarm. The proposed method can ensure the particles to escape from local optima and will achieve the global optimum solution more easily. Also, to enhance the global searching capability of the proposed method, a dynamic updated formula is proposed that will keep a good balance between the local and global searches. To evaluate the merit and efficiency of the proposed DQPSO method, it has been tested on some well-known mathematical test functions and a standard benchmark problem known as Loney’s solenoid design.

Improved Hybrid Particle Swarm Optimizer With Sine-Cosine Acceleration Coefficients For Transient Electromagnetic Inversion

2021 ◽  
Vol 16 ◽  
Author(s):  
Ruiheng Li ◽  
Qiong Zhuang ◽  
Nian Yu ◽  
Ruiyou Li ◽  
Huaiqing Zhang
Keyword(s):  
Learning Strategy ◽  
Particle Swarm ◽  
Population Diversity ◽  
Nonlinear Inversion ◽  
Test Functions ◽  
Particle Swarm Optimizer ◽  
Local Optima ◽  
Hybrid Particle ◽  
Transient Electromagnetic ◽  
Stability And Accuracy

Background: Recently, particle swarm optimization (PSO) has been increasingly used in geophysics due to its simple operation and fast convergence. Objective: However, PSO lacks population diversity and may fall to local optima. Hence, an improved hybrid particle swarm optimizer with sine-cosine acceleration coefficients (IH-PSO-SCAC) is proposed and successfully applied to test functions and in transient electromagnetic (TEM) nonlinear inversion. Method: A reverse learning strategy is applied to optimize population initialization. The sine-cosine acceleration coefficients are utilized for global convergence. Sine mapping is adopted to enhance population diversity during the search process. In addition, the mutation method is used to reduce the probability of premature convergence. Results: The application of IH-PSO-SCAC in the test functions and several simple layered models are demonstrated with satisfactory results in terms of data fit. Two inversions have been carried out to test our algorithm. The first model contains an underground low-resistivity anomaly body and the second model utilized measured data from a profile of the Xishan landslide in Sichuan Province. In both cases, resistivity profiles are obtained, and the inverse problem is solved for verification. Conclusion: The results show that the IH-PSO-SCAC algorithm is practical, can be effectively applied in TEM inversion and is superior to other representative algorithms in terms of stability and accuracy.

Local Best Particle Swarm Optimization Using Crown Jewel Defense Strategy

2018 ◽  
pp. 27-52 ◽  
Author(s):  
Jiarui Zhou ◽  
Junshan Yang ◽  
Ling Lin ◽  
Zexuan Zhu ◽  
Zhen Ji
Keyword(s):  
Particle Swarm Optimization ◽  
High Efficiency ◽  
Particle Swarm ◽  
Global Optimum ◽  
Population Diversity ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Local Optima ◽  
Von Neumann ◽  
Swarm Intelligence Algorithm

Particle swarm optimization (PSO) is a swarm intelligence algorithm well known for its simplicity and high efficiency on various problems. Conventional PSO suffers from premature convergence due to the rapid convergence speed and lack of population diversity. It is easy to get trapped in local optima. For this reason, improvements are made to detect stagnation during the optimization and reactivate the swarm to search towards the global optimum. This chapter imposes the reflecting bound-handling scheme and von Neumann topology on PSO to increase the population diversity. A novel crown jewel defense (CJD) strategy is introduced to restart the swarm when it is trapped in a local optimum region. The resultant algorithm named LCJDPSO-rfl is tested on a group of unimodal and multimodal benchmark functions with rotation and shifting. Experimental results suggest that the LCJDPSO-rfl outperforms state-of-the-art PSO variants on most of the functions.

A Composite Framework of Cuckoo Search and PSO Algorithm

2015 ◽  
Vol 713-715 ◽  
pp. 1491-1494 ◽  
Author(s):  
Zhi Qiang Gao ◽  
Li Xia Liu ◽  
Wei Wei Kong ◽  
Xiao Hong Wang
Keyword(s):  
Cloud Model ◽  
Cuckoo Search ◽  
Pso Algorithm ◽  
Global Optimum ◽  
High Dimensional ◽  
Test Results ◽  
Swarm Optimization ◽  
Local Solutions ◽  
Optimum Solution ◽  
Global Optimum Solution

A novel composite framework of Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithm called CS-PSO is proposed in this paper. In CS-PSO, initialization is substituted by chaotic system, and then Cuckoo shares optimums in the global best solutions pool with particles in PSO to improve parallel cooperation and social interaction. Furthermore, Cloud Model, famous for its outstanding characteristics of the process of transforming qualitative concepts to a set of quantitative numerical values, is adopted to exploit the surrounding of the local solutions obtained from the global best solution pool. Benchmark test results show that, CS-PSO can converge to the global optimum solution rapidly and accurately, compared with other algorithms, especially in high dimensional problems.

A Modified Particle Swarm Optimization Algorithm Based on Proportional Distribution of Particles

2011 ◽  
Vol 181-182 ◽  
pp. 937-942
Author(s):  
Bo Liu ◽  
Hong Xia Pan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Global Optimum ◽  
The Self ◽  
Swarm Optimization ◽  
Local Optima ◽  
Modified Particle Swarm Optimization ◽  
Complex Optimization ◽  
Proportional Distribution

Particle swarm optimization (PSO) is widely used to solve complex optimization problems. However, classical PSO may be trapped in local optima and fails to converge to global optimum. In this paper, the concept of the self particles and the random particles is introduced into classical PSO to keep the particle diversity. All particles are divided into the standard particles, the self particles and the random particles according to special proportion. The feature of the proposed algorithm is analyzed and several testing functions are performed in simulation study. Experimental results show that, the proposed PDPSO algorithm can escape from local minima and significantly enhance the convergence precision.

Super-High Dimension Complex Functions Optimization Using Adaptive Particle Swarm Optimizer

2012 ◽  
Vol 532-533 ◽  
pp. 1830-1835
Author(s):  
Ying Zhang ◽  
Bo Qin Liu ◽  
Han Rong Chen
Keyword(s):  
High Dimension ◽  
Large Scale ◽  
Particle Swarm ◽  
Search Space ◽  
Scale Space ◽  
Global Optimum ◽  
Particle Swarm Optimizer ◽  
Local Optima ◽  
Complex Functions ◽  
Global Optima

Due to the existence of large numbers of local and global optima of super-high dimension complex functions, general Particle Swarm Optimizer (PSO) methods are slow speed on convergence and easy to be trapped in local optima. In this paper, an Adaptive Particle Swarm Optimizer(APSO) is proposed, which employ an adaptive inertia factor and dynamic changes strategy of search space and velocity in each cycle to plan large-scale space global search and refined local search as a whole according to the fitness change of swarm in optimization process of the functions, and to quicken convergence speed, avoid premature problem, economize computational expenses, and obtain global optimum. We test the proposed algorithm and compare it with other published methods on several super-high dimension complex functions, the experimental results demonstrate that this revised algorithm can rapidly converge at high quality solutions.

scholarly journals Enhanced Comprehensive Learning Particle Swarm Optimization with Dimensional Independent and Adaptive Parameters

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Xiang Yu ◽  
Yu Qiao
Keyword(s):  
Particle Swarm Optimization ◽  
Adaptive Learning ◽  
Particle Swarm ◽  
Search Space ◽  
Global Optimum ◽  
Independent Learning ◽  
Swarm Optimization ◽  
Convergence Performance ◽  
Learning Probability ◽  
Optimum Solution

Comprehensive learning particle swarm optimization (CLPSO) and enhanced CLPSO (ECLPSO) are two literature metaheuristics for global optimization. ECLPSO significantly improves the exploitation and convergence performance of CLPSO by perturbation-based exploitation and adaptive learning probabilities. However, ECLPSO still cannot locate the global optimum or find a near-optimum solution for a number of problems. In this paper, we study further bettering the exploration performance of ECLPSO. We propose to assign an independent inertia weight and an independent acceleration coefficient corresponding to each dimension of the search space, as well as an independent learning probability for each particle on each dimension. Like ECLPSO, a normative interval bounded by the minimum and maximum personal best positions is determined with respect to each dimension in each generation. The dimensional independent maximum velocities, inertia weights, acceleration coefficients, and learning probabilities are proposed to be adaptively updated based on the dimensional normative intervals in order to facilitate exploration, exploitation, and convergence, particularly exploration. Our proposed metaheuristic, called adaptive CLPSO (ACLPSO), is evaluated on various benchmark functions. Experimental results demonstrate that the dimensional independent and adaptive maximum velocities, inertia weights, acceleration coefficients, and learning probabilities help to significantly mend ECLPSO’s exploration performance, and ACLPSO is able to derive the global optimum or a near-optimum solution on all the benchmark functions for all the runs with parameters appropriately set.

Tracking Multiple Optima in Dynamic Environments by Quantum-Behavior Particle Swarm Using Speciation

2012 ◽  
Vol 3 (1) ◽  
pp. 55-76 ◽  
Author(s):  
Ji Zhao ◽  
Jun Sun ◽  
Vasile Palade
Keyword(s):  
Optimization Problems ◽  
Particle Swarm ◽  
Dynamic Environments ◽  
Multimodal Optimization ◽  
Complex Dynamic ◽  
Particle Swarm Optimizer ◽  
Swarm Optimization ◽  
Local Optima ◽  
Dynamic Function ◽  
Quantum Behavior

This paper presents an improved Quantum-behaved Particle Swarm Optimization, namely the Species-Based QPSO (SQPSO), using the notion of species for solving optimization problems with multiple peaks from the complex dynamic environments. In the proposed SQPSO algorithm, the swarm population is divided into species (subpopulations) based on their similarities. Each species is grouped around a dominating particle called species seed. Over successive iterations, species are able to simultaneously optimize towards multiple optima by using the QPSO procedure, so that each of the peaks can be definitely searched in parallel, regardless of whether they are global or local optima. A number of experiments are performed to test the performance of the SQPSO algorithm. The environment used in the experiments is generated by Dynamic Function # 1(DF1). The experimental results show that the SQPSO is more adaptive than the Species-Based Particle Swarm Optimizer (SPSO) in dealing with multimodal optimization in dynamic environments.

DIVERSITY-BASED INFORMATION EXCHANGE AMONG MULTIPLE SWARMS IN PARTICLE SWARM OPTIMIZATION

2008 ◽  
Vol 07 (01) ◽  
pp. 57-75 ◽  
Author(s):  
GARY G. YEN ◽  
MOAYED DANESHYARI
Keyword(s):  
Particle Swarm Optimization ◽  
Information Exchange ◽  
Particle Swarm ◽  
Evolutionary Search ◽  
Test Functions ◽  
Swarm Optimization ◽  
Local Optima ◽  
Common Interests ◽  
Multiple Swarms ◽  
Benchmark Test Functions

This paper proposes a method to exchange information among multiple swarms in particle swarm optimization (PSO) to facilitate evolutionary search. The algorithm is developed to solve problems having landscapes with a large number of local optima. Each swarm maintains two sets of particles; one set includes the particles to be shared with other swarms, while the other involves the particles to be replaced by individuals from other swarms. The proposed algorithm also provides a new design to search for neighboring swarms in order to share common interests among the swarm's neighborhood. The particle's movement is according to one variation of PSO with three basic terms, each one to lead the particles toward the best particle in the swarm, in the neighborhood, and in the whole population. Demonstrated through a suite of benchmark test functions, the proposed algorithm shows competitive performance with improved convergence speed.

The Cuckoo Search Algorithm Based on Dynamic Grouping to Adjust Flight Scale

2014 ◽  
Vol 543-547 ◽  
pp. 1822-1826 ◽  
Author(s):  
Yi Ge Xue ◽  
Hui Wen Deng
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Global Optimum ◽  
Cuckoo Search Algorithm ◽  
Simulation Experiments ◽  
Swarm Optimization ◽  
The Individual ◽  
Optimum Solution ◽  
Average Fitness ◽  
Global Optimum Solution

The cuckoo search (CS) algorithm is a very efficient swarm optimization algorithm. Based on CS, a cuckoo search algorithm based on dynamic grouping to adjust flight scale (DGCS) is proposed: All cuckoos are divided into three groups according to the fitness of the individual and the average fitness of the population, then different flight scale is adopted dynamically for each group. Simulation experiments show that the DGCS can quickly converge to the global optimum solution, and has better optimization performance.

scholarly journals New Evolutionary-Based Techniques for Image Registration

2019 ◽  
Vol 9 (1) ◽  
pp. 176 ◽  
Author(s):  
Catalina-Lucia Cocianu ◽  
Alexandru Stan
Keyword(s):  
Recognition Rate ◽  
Global Optimum ◽  
Swarm Optimization ◽  
Trade Off ◽  
Accelerated Particle Swarm Optimization ◽  
Speed Up ◽  
Signature Recognition ◽  
Optimum Solution ◽  
Global Optimum Solution

The work reported in this paper aims at the development of evolutionary algorithms to register images for signature recognition purposes. We propose and develop several registration methods in order to obtain accurate and fast algorithms. First, we introduce two variants of the firefly method that proved to have excellent accuracy and fair run times. In order to speed up the computation, we propose two variants of Accelerated Particle Swarm Optimization (APSO) method. The resulted algorithms are significantly faster than the firefly-based ones, but the recognition rates are a little bit lower. In order to find a trade-off between the recognition rate and the computational complexity of the algorithms, we developed a hybrid method that combines the ability of auto-adaptive Evolution Strategies (ES) search to discover a global optimum solution with the strong quick convergence ability of APSO. The accuracy and the efficiency of the resulted algorithms have been experimentally proved by conducting a long series of tests on various pairs of signature images. The comparative analysis concerning the quality of the proposed methods together with conclusions and suggestions for further developments are provided in the final part of the paper.

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