scholarly journals A Multiobjective Particle Swarm Optimization Algorithm Based on Competition Mechanism and Gaussian Variation

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-23 ◽  
Author(s):  
Hongli Yu ◽  
Yuelin Gao ◽  
Jincheng Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Population Diversity ◽  
Distance Method ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Mutation Strategy ◽  
Speed Up ◽  
Particle Parameters

In order to solve the shortcomings of particle swarm optimization (PSO) in solving multiobjective optimization problems, an improved multiobjective particle swarm optimization (IMOPSO) algorithm is proposed. In this study, the competitive strategy was introduced into the construction process of Pareto external archives to speed up the search process of nondominated solutions, thereby increasing the speed of the establishment of Pareto external archives. In addition, the descending order of crowding distance method is used to limit the size of external archives and dynamically adjust particle parameters; in order to solve the problem of insufficient population diversity in the later stage of algorithm iteration, time-varying Gaussian mutation strategy is used to mutate the particles in external archives to improve diversity. The simulation experiment results show that the improved algorithm has better convergence and stability than the other compared algorithms.

scholarly journals Knowledge-guided multiobjective particle swarm optimization with fusion learning strategies

2021 ◽  
Author(s):  
Wei Li ◽  
Xiang Meng ◽  
Ying Huang ◽  
Soroosh Mahmoodi
Keyword(s):  
Particle Swarm Optimization ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Particle Swarm ◽  
Population Diversity ◽  
Selection Strategy ◽  
Rapid Loss ◽  
Solution Quality ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization

AbstractMultiobjective particle swarm optimization (MOPSO) algorithm faces the difficulty of prematurity and insufficient diversity due to the selection of inappropriate leaders and inefficient evolution strategies. Therefore, to circumvent the rapid loss of population diversity and premature convergence in MOPSO, this paper proposes a knowledge-guided multiobjective particle swarm optimization using fusion learning strategies (KGMOPSO), in which an improved leadership selection strategy based on knowledge utilization is presented to select the appropriate global leader for improving the convergence ability of the algorithm. Furthermore, the similarity between different individuals is dynamically measured to detect the diversity of the current population, and a diversity-enhanced learning strategy is proposed to prevent the rapid loss of population diversity. Additionally, a maximum and minimum crowding distance strategy is employed to obtain excellent nondominated solutions. The proposed KGMOPSO algorithm is evaluated by comparisons with the existing state-of-the-art multiobjective optimization algorithms on the ZDT and DTLZ test instances. Experimental results illustrate that KGMOPSO is superior to other multiobjective algorithms with regard to solution quality and diversity maintenance.

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 Multi/Many-Objective Particle Swarm Optimization Algorithm Based on Competition Mechanism

2020 ◽  
Vol 2020 ◽  
pp. 1-26
Author(s):  
Wusi Yang ◽  
Li Chen ◽  
Yi Wang ◽  
Maosheng Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Benchmark Problems ◽  
Swarm Optimization ◽  
Competition Mechanism ◽  
Multiobjective Particle Swarm Optimization

The recently proposed multiobjective particle swarm optimization algorithm based on competition mechanism algorithm cannot effectively deal with many-objective optimization problems, which is characterized by relatively poor convergence and diversity, and long computing runtime. In this paper, a novel multi/many-objective particle swarm optimization algorithm based on competition mechanism is proposed, which maintains population diversity by the maximum and minimum angle between ordinary and extreme individuals. And the recently proposed θ-dominance is adopted to further enhance the performance of the algorithm. The proposed algorithm is evaluated on the standard benchmark problems DTLZ, WFG, and UF1-9 and compared with the four recently proposed multiobjective particle swarm optimization algorithms and four state-of-the-art many-objective evolutionary optimization algorithms. The experimental results indicate that the proposed algorithm has better convergence and diversity, and its performance is superior to other comparative algorithms on most test instances.

The Shortest Path Optimization Based on Mutation Particle Swarm Optimization Algorithm

2014 ◽  
Vol 1049-1050 ◽  
pp. 1690-1693 ◽  
Author(s):  
Juan Li
Keyword(s):  
Particle Swarm Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Path Optimization ◽  
Adaptive Mutation ◽  
Swarm Optimization ◽  
Mutation Strategy

The traditional evolutionary algorithm is cannot converge faster to solve the path optimization problems, and the path that is computed is not the shortest path, in allusion to the disadvantage of this algorithm, a mutation particle swarm optimization algorithm is proposed. The algorithm introduces the adaptive mutation strategy, and accelerated the speed to search for the global optimal solution. For seven examples experiment in standard database, the result shows that the algorithm is more efficient..

Power Consumption Optimization of a Building Using Multiobjective Particle Swarm Optimization

2015 ◽  
Vol 72 (2) ◽  
Author(s):  
Ahmad Faiz Ab Rahman ◽  
Hazlina Selamat ◽  
Fatimah Sham Ismail ◽  
Nurulaqilla Khamis
Keyword(s):  
Particle Swarm Optimization ◽  
Performance Metrics ◽  
Optimization Problems ◽  
Low Cost ◽  
Particle Swarm ◽  
Sorting Algorithm ◽  
Swarm Optimization ◽  
Multiobjective Particle Swarm Optimization ◽  
Building Energy Optimization ◽  
Save Energy

This paper discusses the development of a building energy optimization algorithm by using multiobjective Particle Swarm Optimization for a building. Particle Swarm Optimization is a well known algorithm that is proven to be effective in many complex optimization problems. Multiobjective PSO is developed by utilizing non-dominated sorting algorithm in tandem with majority-based selection algorithm. The optimizer is written by using MATLAB alongside its GUI interface. Results are then analyzed by using the Binh and Korn benchmark test and natural distance performance metrics. From the results, the optimizer is capable to minimize up to 42 percent of energy consumption and lowering the electricity bills up to 43 percent, while still maintaining comfort at more than 95 percent as well. With this, building owner can save energy with a low-cost and simple solution. 

scholarly journals Dynamic Shannon Performance in a Multiobjective Particle Swarm Optimization

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 827 ◽  
Author(s):  
E. J. Solteiro Pires ◽  
J. A. Tenreiro Machado ◽  
P. B. de Moura Oliveira
Keyword(s):  
Particle Swarm Optimization ◽  
Collective Behavior ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
Multiobjective Particle Swarm Optimization ◽  
Multiobjective Algorithms ◽  
Average Fitness

Particle swarm optimization (PSO) is a search algorithm inspired by the collective behavior of flocking birds and fishes. This algorithm is widely adopted for solving optimization problems involving one objective. The evaluation of the PSO progress is usually measured by the fitness of the best particle and the average fitness of the particles. When several objectives are considered, the PSO may incorporate distinct strategies to preserve nondominated solutions along the iterations. The performance of the multiobjective PSO (MOPSO) is usually evaluated by considering the resulting swarm at the end of the algorithm. In this paper, two indices based on the Shannon entropy are presented, to study the swarm dynamic evolution during the MOPSO execution. The results show that both indices are useful for analyzing the diversity and convergence of multiobjective algorithms.

scholarly journals Improved Particle Swarm Optimization Algorithm Based on Last-Eliminated Principle and Enhanced Information Sharing

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Xueying Lv ◽  
Yitian Wang ◽  
Junyi Deng ◽  
Guanyu Zhang ◽  
Liu Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Information Sharing ◽  
Optimization Problems ◽  
Optimization Algorithms ◽  
Particle Swarm ◽  
Population Diversity ◽  
Search Performance ◽  
Information Communication ◽  
Swarm Optimization ◽  
Classical Optimization

In this study, an improved eliminate particle swarm optimization (IEPSO) is proposed on the basis of the last-eliminated principle to solve optimization problems in engineering design. During optimization, the IEPSO enhances information communication among populations and maintains population diversity to overcome the limitations of classical optimization algorithms in solving multiparameter, strong coupling, and nonlinear engineering optimization problems. These limitations include advanced convergence and the tendency to easily fall into local optimization. The parameters involved in the imported “local-global information sharing” term are analyzed, and the principle of parameter selection for performance is determined. The performances of the IEPSO and classical optimization algorithms are then tested by using multiple sets of classical functions to verify the global search performance of the IEPSO. The simulation test results and those of the improved classical optimization algorithms are compared and analyzed to verify the advanced performance of the IEPSO algorithm.

scholarly journals Many-Objective Particle Swarm Optimization Algorithm Based on New Fitness Allocation and Multiple Cooperative Strategies

2021 ◽  
Vol 15 (4) ◽  
pp. 1-23
Author(s):  
Weiwei Yu ◽  
Li Zhang ◽  
Chengwang Xie
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mean Value ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Cooperative Strategies ◽  
Mutation Strategy

Many-objective optimization problems (MaOPs) refer to those multi-objective problems (MOPs) withmore than three objectives. In order to solve MaOPs, a multi-objective particle swarm optimization algorithm based on new fitness assignment and multi cooperation strategy(FAMSHMPSO) is proposed. Firstly, this paper proposes a new fitness allocation method based on fuzzy information theory to enhance the convergence of the algorithm. Then a new multi criteria mutation strategy is introduced to disturb the population and improve the diversity of the algorithm. Finally, the external files are maintained by the three-point shortest path method, which improves the quality of the solution. The performance of FAMSHMPSO algorithm is evaluated by evaluating the mean value, standard deviation and IGD+ index of the target value on dtlz test function set of different targets of FAMSHMPSO algorithm and other five representative multi-objective evolutionary algorithms. The experimental results show that FAMSHMPSO algorithm has obvious performance advantages in convergence, diversity and robustness.

An Improved Particle Swarm Optimization Algorithm for Constrained Chemical Engineering Optimization

2014 ◽  
Vol 962-965 ◽  
pp. 746-750
Author(s):  
Jian Chun Yang ◽  
Wen Long
Keyword(s):  
Particle Swarm Optimization ◽  
Chemical Engineering ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Engineering Optimization ◽  
Test Functions ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Mutation Strategy

An improved particle swarm optimization (IPSO) is proposed for solving constrained numerical and engineering optimization problems in this paper. In proposed algorithm, an initialization strategy based on the opposition learning is applied to diversity the initial particles in the search space. Self-adaptive inertia weight is introduced to balance the ability of exploration and exploitation. Diversity mutation strategy is employed for best of particles to introduce diversity in the swarm space. Simulation results and comparisons with other algorithms using two benchmark constrained test functions and chemical engineering optimization problem are provided.

A Local Best Particle Swarm Optimization Based on Crown Jewel Defense Strategy

2015 ◽  
Vol 6 (1) ◽  
pp. 41-63 ◽  
Author(s):  
Jiarui Zhou ◽  
Junshan Yang ◽  
Ling Lin ◽  
Zexuan Zhu ◽  
Zhen Ji
Keyword(s):  
Particle Swarm Optimization ◽  
High Efficiency ◽  
Optimization Problems ◽  
State Of The Art ◽  
Particle Swarm ◽  
Global Optimum ◽  
Population Diversity ◽  
Swarm Optimization ◽  
Von Neumann ◽  
Swarm Intelligence Algorithm

Particle swarm optimization (PSO) is a swarm intelligence algorithm well known for its simplicity and high efficiency on various optimization problems. Conventional PSO suffers from premature convergence due to the rapid convergence speed and lack of population diversity. PSO is easy to get trapped in local optimal, which largely deteriorates its performance. It is natural to detect stagnation during the optimization, and reactivate the swarm to search towards the global optimum. In this work the authors impose the reflecting bound-handling scheme and von Neumann topology on PSO to increase the population diversity. A novel Crown Jewel Defense (CJD) strategy is also introduced to restart the swarm when it is trapped in a local optimal. The resultant algorithm named LCJDPSO-rfl is tested on a group of unimodal and multimodal benchmark functions with rotation and shifting, and compared with other state-of-the-art PSO variants. The experimental results demonstrate stability and efficiency of LCJDPSO-rfl on most of the functions.

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