scholarly journals Chicken Swarm Optimization Based on Elite Opposition-Based Learning

2017 ◽  
Vol 2017 ◽  
pp. 1-20 ◽  
Author(s):  
Chiwen Qu ◽  
Shi’an Zhao ◽  
Yanming Fu ◽  
Wei He
Keyword(s):  
Optimization Problems ◽  
Local Development ◽  
Random Search ◽  
Standard Test ◽  
Population Diversity ◽  
Local Optimum ◽  
Basic Algorithm ◽  
Swarm Optimization ◽  
Opposition Based Learning ◽  
Chicken Swarm Optimization

Chicken swarm optimization is a new intelligent bionic algorithm, simulating the chicken swarm searching for food in nature. Basic algorithm is likely to fall into a local optimum and has a slow convergence rate. Aiming at these deficiencies, an improved chicken swarm optimization algorithm based on elite opposition-based learning is proposed. In cock swarm, random search based on adaptive t distribution is adopted to replace that based on Gaussian distribution so as to balance the global exploitation ability and local development ability of the algorithm. In hen swarm, elite opposition-based learning is introduced to promote the population diversity. Dimension-by-dimension greedy search mode is used to do local search for individual of optimal chicken swarm in order to improve optimization precision. According to the test results of 18 standard test functions and 2 engineering structure optimization problems, this algorithm has better effect on optimization precision and speed compared with basic chicken algorithm and other intelligent optimization algorithms.

Improved Particle Swarm Optimization Algorithm and its Application Based on the Aggregation Degree

2013 ◽  
Vol 427-429 ◽  
pp. 1934-1938
Author(s):  
Zhong Rong Zhang ◽  
Jin Peng Liu ◽  
Ke De Fei ◽  
Zhao Shan Niu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Function Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization

The aim is to improve the convergence of the algorithm, and increase the population diversity. Adaptively particles of groups fallen into local optimum is adjusted in order to realize global optimal. by judging groups spatial location of concentration and fitness variance. At the same time, the global factors are adjusted dynamically with the action of the current particle fitness. Four typical function optimization problems are drawn into simulation experiment. The results show that the improved particle swarm optimization algorithm is convergent, robust and accurate.

scholarly journals An improved Wolf pack algorithm for optimization problems: Design and evaluation

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0254239
Author(s):  
Xuan Chen ◽  
Feng Cheng ◽  
Cong Liu ◽  
Long Cheng ◽  
Yin Mao
Keyword(s):  
Optimization Problems ◽  
Local Development ◽  
Initial Population ◽  
Local Optimum ◽  
Engineering Optimization ◽  
Test Functions ◽  
Opposition Based Learning ◽  
Global And Local ◽  
Swarm Intelligence Algorithm ◽  
Search Capability

Wolf Pack Algorithm (WPA) is a swarm intelligence algorithm that simulates the food searching process of wolves. It is widely used in various engineering optimization problems due to its global convergence and computational robustness. However, the algorithm has some weaknesses such as low convergence speed and easily falling into local optimum. To tackle the problems, we introduce an improved approach called OGL-WPA in this work, based on the employments of Opposition-based learning and Genetic algorithm with Levy’s flight. Specifically, in OGL-WPA, the population of wolves is initialized by opposition-based learning to maintain the diversity of the initial population during global search. Meanwhile, the leader wolf is selected by genetic algorithm to avoid falling into local optimum and the round-up behavior is optimized by Levy’s flight to coordinate the global exploration and local development capabilities. We present the detailed design of our algorithm and compare it with some other nature-inspired metaheuristic algorithms using various classical test functions. The experimental results show that the proposed algorithm has better global and local search capability, especially in the presence of multi-peak and high-dimensional functions.

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.

scholarly journals Hybrid Algorithm Based on Ant Colony Optimization and Simulated Annealing Applied to the Dynamic Traveling Salesman Problem

Entropy ◽  
2020 ◽  
Vol 22 (8) ◽  
pp. 884
Author(s):  
Petr Stodola ◽  
Karel Michenka ◽  
Jan Nohel ◽  
Marian Rybanský
Keyword(s):  
Simulated Annealing ◽  
Ant Colony Optimization ◽  
Traveling Salesman Problem ◽  
Optimization Problems ◽  
Dynamic Environment ◽  
Population Diversity ◽  
Traveling Salesman ◽  
Ant Colony ◽  
Local Optimum ◽  
Use Of Knowledge

The dynamic traveling salesman problem (DTSP) falls under the category of combinatorial dynamic optimization problems. The DTSP is composed of a primary TSP sub-problem and a series of TSP iterations; each iteration is created by changing the previous iteration. In this article, a novel hybrid metaheuristic algorithm is proposed for the DTSP. This algorithm combines two metaheuristic principles, specifically ant colony optimization (ACO) and simulated annealing (SA). Moreover, the algorithm exploits knowledge about the dynamic changes by transferring the information gathered in previous iterations in the form of a pheromone matrix. The significance of the hybridization, as well as the use of knowledge about the dynamic environment, is examined and validated on benchmark instances including small, medium, and large DTSP problems. The results are compared to the four other state-of-the-art metaheuristic approaches with the conclusion that they are significantly outperformed by the proposed algorithm. Furthermore, the behavior of the algorithm is analyzed from various points of view (including, for example, convergence speed to local optimum, progress of population diversity during optimization, and time dependence and computational complexity).

Improved Fractional-Order PSO for PID Tuning

2021 ◽  
pp. 2159016
Author(s):  
Shoubao Su ◽  
Zhaorui Zhai ◽  
Chishe Wang ◽  
Kaimeng Ding
Keyword(s):  
Particle Swarm Optimization ◽  
Fractional Order ◽  
Particle Swarm ◽  
Population Diversity ◽  
Time Factor ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Pid Tuning ◽  
Convergence Performance

The traditional fractional-order particle swarm optimization (FOPSO) algorithm depends on the fractional order [Formula: see text], and it is easy to fall into local optimum. To overcome these disadvantages, a novel perspective with PID gains tuning procedure is proposed by combining the time factor with FOPSO, i.e. a new fractional-order particle swarm optimization called TFFV-PSO, which reduces the dependence on the fractional order to enhance the ability of particles to escape from local optimums. According to its influence on the performance of the algorithm, the time factor is varied with population diversity parameters to balance the exploration and exploitation capabilities of the particle swarm, so as to adjust the convergence speed of the algorithm, then it follows that a better convergence performance will be obtained. The improved method is tested on several benchmark functions and applied to tune the PID controller parameters. The experimental results and the comparison with previous other methods show that our proposed TFFV-PSO provides an adequate velocity of convergence and a satisfying accuracy, as well as even better robustness.

An improved lion swarm optimization for parameters identification of photovoltaic cell models

2019 ◽  
Vol 42 (6) ◽  
pp. 1191-1203
Author(s):  
Zhong-qiang Wu ◽  
Zong-kui Xie ◽  
Chong-yang Liu
Keyword(s):  
Parameter Identification ◽  
Cell Model ◽  
Chaotic Map ◽  
Photovoltaic Cell ◽  
Standard Test ◽  
Local Optimum ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Test Function ◽  
Adaptive Parameter

In this paper, a parameter identification method of photovoltaic cell model based on improved lion swarm optimization is presented. Lion swarm optimization is a novel intelligent algorithm proposed in recent years, but it has problems such as local optimum and slow convergence. To overcome such limitations, we can combine the tent chaotic map, adaptive parameter and chaotic search strategy to further improve the search ability of the algorithm and avoid trapping in local optimum. The simulation of standard test function shows that the performance of improved lion swarm algorithm is superior to the other six algorithms. Then the algorithm is applied to the parameter identification of photovoltaic cells under two kinds of models and different irradiance, the simulation results verify the superiority and effectiveness of the improved lion swarm optimization in the application of photovoltaic cell parameter identification.

scholarly journals GIS-Based Niche Hybrid Bat Algorithm for Solving Optimal Spatial Search

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guoming Du ◽  
Yangbo Chen ◽  
Wei Sun
Keyword(s):  
Optimization Problems ◽  
Bat Algorithm ◽  
Population Diversity ◽  
Premature Convergence ◽  
Local Optimum ◽  
Guangzhou City ◽  
Location Allocation ◽  
Geographic Space ◽  
Spatial Search ◽  
Gis Technique

Complex nonlinear optimization problems are involved in optimal spatial search, such as location allocation problems that occur in multidimensional geographic space. Such search problems are generally difficult to solve by using traditional methods. The bat algorithm (BA) is an effective method for solving optimization problems. However, the solution of the standard BA is easily trapped at one of its local optimum values. The main cause of premature convergence is the loss of diversity in the population. The niche technique is an effective method to maintain the population diversity, to enhance the exploration of the new search domains, and to avoid premature convergence. In this paper, a geographic information system- (GIS-) based niche hybrid bat algorithm (NHBA) is proposed for solving the optimal spatial search. The NHBA is able to avoid the premature convergence and obtain the global optimal values. The GIS technique provides robust support for processing a substantial amount of geographical data. A case in Fangcun District, Guangzhou City, China, is used to test the NHBA. The comparative experiments illustrate that the BA, GA, FA, PSO, and NHBA algorithms outperform the brute-force algorithm in terms of computational efficiency, and the optimal solutions are more easily obtained with NHBA than with BA, GA, FA, and PSO. Moreover, the precision of NHBA is higher and the convergence of NHBA is faster than those of the other algorithms under the same conditions.

scholarly journals Improved Adaptive Holonic Particle Swarm Optimization

2019 ◽  
Vol 2019 ◽  
pp. 1-22 ◽  
Author(s):  
Hao Li ◽  
Hongbin Jin ◽  
Hanzhong Wang ◽  
Yanyan Ma
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Pso Algorithm ◽  
Standard Test ◽  
Local Optimum ◽  
Swarm Optimization ◽  
State Switching ◽  
Grouping Strategy ◽  
Number Of Iterations

For the first time , the Holonic Particle Swarm Optimization (HPSO ) algorithm applies multiagent theory about the improvement in the PSO algorithm and achieved good results. In order to further improve the performance of the algorithm, this paper proposes an improved Adaptive Holonic Particle Swarm Optimization (AHPSO) algorithm. Firstly, a brief review of the HPSO algorithm is carried out, and the HPSO algorithm can be further studied in three aspects: grouping strategy, iteration number setting, and state switching discrimination. The HPSO algorithm uses an approximately uniform grouping strategy that is the simplest but does not consider the connections between particles. And if the particles with larger or smaller differences are grouped together in different search stages, the search efficiency will be improved. Therefore, this paper proposes a grouping strategy based on information entropy and system clustering and combines two grouping strategies with corresponding search methods. The performance of the HPSO algorithm depends on the setting of the number of iterations. If it is too small, it is difficult to search for the optimal and it wastes so many computing resources. Therefore, this paper constructs an adaptive termination condition that causes the particles to terminate spontaneously after convergence. The HPSO algorithm only performs a conversion from extensive search to exact search and still has the potential to fall into local optimum. This paper proposes a state switching condition to improve the probability that the algorithm jumps out of the local optimum. Finally, AHPSO and HPSO are compared by using 22 groups of standard test functions. AHPSO is faster in convergence than HPSO, and the number of iterations of AHPSO convergence is employed in HPSO. At this point, there exists a large gap between HPSO and the optimal solution, i.e., AHPSO can have better algorithm efficiency without setting the number of iterations.

Design and Simulation of an Improved Particle Swarm Optimization

2010 ◽  
Vol 20-23 ◽  
pp. 1280-1285
Author(s):  
Jian Xiang Wei ◽  
Yue Hong Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Dimensional Space ◽  
Numerical Test ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Population Diversity ◽  
Test Problems ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Improved Pso

The particle swarm optimization (PSO) algorithm is a new population search strategy, which has exhibited good performance through well-known numerical test problems. However, it is easy to trap into local optimum because the population diversity becomes worse during the evolution. In order to overcome the shortcoming of the PSO, this paper proposes an improved PSO based on the symmetry distribution of the particle space position. From the research of particle movement in high dimensional space, we can see: the more symmetric of the particle distribution, the bigger probability can the algorithm be during converging to the global optimization solution. A novel population diversity function is put forward and an adjustment algorithm is put into the basic PSO. The steps of the proposed algorithm are given in detail. With two typical benchmark functions, the experimental results show the improved PSO has better convergence precision than the basic PSO.

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