scholarly journals A Decomposition-Based Unified Evolutionary Algorithm for Many-Objective Problems Using Particle Swarm Optimization

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Anqi Pan ◽  
Hongjun Tian ◽  
Lei Wang ◽  
Qidi Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Particle Swarm ◽  
The Other ◽  
High Dimensional ◽  
Swarm Optimization ◽  
Multiobjective Optimization Problems ◽  
Reconstruction Strategy ◽  
Leader Selection

Evolutionary algorithms have proved to be efficient approaches in pursuing optimum solutions of multiobjective optimization problems with the number of objectives equal to or less than three. However, the searching performance degenerates in high-dimensional objective optimizations. In this paper we propose an algorithm for many-objective optimization with particle swarm optimization as the underlying metaheuristic technique. In the proposed algorithm, the objectives are decomposed and reconstructed using discrete decoupling strategy, and the subgroup procedures are integrated into unified coevolution strategy. The proposed algorithm consists of inner and outer evolutionary processes, together with adaptive factor μ, to maintain convergence and diversity. At the same time, a designed repository reconstruction strategy and improved leader selection techniques of MOPSO are introduced. The comparative experimental results prove that the proposed UMOPSO-D outperforms the other six algorithms, including four common used algorithms and two newly proposed algorithms based on decomposition, especially in high-dimensional targets.

Stability Enhancement of Transfer Function by Ant Colony Optimization and Particle Swarm Optimization Algorithm

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Chaman Yadav ◽  
Prabha Singh ◽  
Jaya Mishra ◽  
Kushal Tiwari ◽  
Shashank Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Ant Colony Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Ant Colony ◽  
Swarm Optimization ◽  
The Way

This paper presents the concepts of three evolutionary algorithms i.e, ant colony optimization and particle swarm optimization algorithm. An evolutionary algorithm copies the way how evolution occurs in the nature. There are various types of evolutionary algorithms. This paper focuses on ACO and PSO algorithms. ACO provides solution to various optimization problems. It follows the principle of survival of the fittest. Various problems such as knapsack problem, TSP(travelling salesman problem) can be solved using genetic algorithm. Ant colony optimization is a heuristic algorithm which follows the behaviour of ants i.e., the way ants seek food in their environment by starting from their nest. Particle swarm optimization algorithm (PSO) is also an optimization algorithm which also uses a method of searching using some heuristics.

scholarly journals Global Particle Swarm Optimization for High Dimension Numerical Functions Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
J. J. Jamian ◽  
M. N. Abdullah ◽  
H. Mokhlis ◽  
M. W. Mustafa ◽  
A. H. A. Bakar
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Function Optimization ◽  
High Dimensional ◽  
Local Optimum ◽  
Mathematical Function ◽  
Numerical Function ◽  
Swarm Optimization ◽  
Particle Position

The Particle Swarm Optimization (PSO) Algorithm is a popular optimization method that is widely used in various applications, due to its simplicity and capability in obtaining optimal results. However, ordinary PSOs may be trapped in the local optimal point, especially in high dimensional problems. To overcome this problem, an efficient Global Particle Swarm Optimization (GPSO) algorithm is proposed in this paper, based on a new updated strategy of the particle position. This is done through sharing information of particle position between the dimensions (variables) at any iteration. The strategy can enhance the exploration capability of the GPSO algorithm to determine the optimum global solution and avoid traps at the local optimum. The proposed GPSO algorithm is validated on a 12-benchmark mathematical function and compared with three different types of PSO techniques. The performance of this algorithm is measured based on the solutions’ quality, convergence characteristics, and their robustness after 50 trials. The simulation results showed that the new updated strategy in GPSO assists in realizing a better optimum solution with the smallest standard deviation value compared to other techniques. It can be concluded that the proposed GPSO method is a superior technique for solving high dimensional numerical function optimization problems.

scholarly journals A Multimodal Improved Particle Swarm Optimization for High Dimensional Problems in Electromagnetic Devices

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8575
Author(s):  
Rehan Ali Khan ◽  
Shiyou Yang ◽  
Shafiullah Khan ◽  
Shah Fahad ◽  
Kalimullah
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Magnetic Energy ◽  
Particle Swarm ◽  
Optimal Solution ◽  
High Dimensional ◽  
Adaptive Mutation ◽  
Dynamic Factor ◽  
Energy Storage Devices ◽  
Swarm Optimization

Particle Swarm Optimization (PSO) is a member of the swarm intelligence-based on a metaheuristic approach which is inspired by the natural deeds of bird flocking and fish schooling. In comparison to other traditional methods, the model of PSO is widely recognized as a simple algorithm and easy to implement. However, the traditional PSO’s have two primary issues: premature convergence and loss of diversity. These problems arise at the latter stages of the evolution process when dealing with high-dimensional, complex and electromagnetic inverse problems. To address these types of issues in the PSO approach, we proposed an Improved PSO (IPSO) which employs a dynamic control parameter as well as an adaptive mutation mechanism. The main proposal of the novel adaptive mutation operator is to prevent the diversity loss of the optimization process while the dynamic factor comprises the balance between exploration and exploitation in the search domain. The experimental outcomes achieved by solving complicated and extremely high-dimensional optimization problems were also validated on superconducting magnetic energy storage devices (SMES). According to numerical and experimental analysis, the IPSO delivers a better optimal solution than the other solutions described, particularly in the early computational evaluation of the generation.

Strength Pareto Particle Swarm Optimization and Hybrid EA-PSO for Multi-Objective Optimization

2010 ◽  
Vol 18 (1) ◽  
pp. 127-156 ◽  
Author(s):  
Ahmed Elhossini ◽  
Shawki Areibi ◽  
Robert Dony
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Strength Pareto Evolutionary Algorithm ◽  
Hybrid Forms

This paper proposes an efficient particle swarm optimization (PSO) technique that can handle multi-objective optimization problems. It is based on the strength Pareto approach originally used in evolutionary algorithms (EA). The proposed modified particle swarm algorithm is used to build three hybrid EA-PSO algorithms to solve different multi-objective optimization problems. This algorithm and its hybrid forms are tested using seven benchmarks from the literature and the results are compared to the strength Pareto evolutionary algorithm (SPEA2) and a competitive multi-objective PSO using several metrics. The proposed algorithm shows a slower convergence, compared to the other algorithms, but requires less CPU time. Combining PSO and evolutionary algorithms leads to superior hybrid algorithms that outperform SPEA2, the competitive multi-objective PSO (MO-PSO), and the proposed strength Pareto PSO based on different metrics.

Minimum-Zone Circularity Evaluation Using Particle Swarm Optimization

2005 ◽  
Author(s):  
Hemant Ramaswami ◽  
Yashpal Kovvur ◽  
Sam Anand
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
High Speed ◽  
Optimization Problems ◽  
Linear Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Non Linear ◽  
Minimum Zone ◽  
Accuracy And Repeatability

Robust and accurate evaluation of form tolerances is of paramount importance in today’s world of precision engineering. Present-day Coordinate Measuring Machines (CMMs) operate at high speed and have a high degree of accuracy and repeatability which are capable of meeting the stringent measurement requirements. However, the evaluation algorithms used in conjunction with them are not robust and accurate enough, because of the highly non-linear nature of the minimum-zone circularity formulation. Evolutionary algorithms have proved effective in solving constrained non-linear optimization problems. In this paper, Particle Swarm Optimization (PSO), which is one of the most recent and popular evolutionary algorithms, is employed to evaluate the minimum-zone circularity. The PSO approach imitates the social behavior of organisms such as bird flocking and fish schooling. It differs from other well-known Evolutionary Algorithms (EA) in that each particle of the population, called the swarm, adjusts its trajectory toward its own previous best position, and toward the previous best position attained by any member of its topological neighborhood. The constrained nonlinear model is rewritten as an unconstrained non-linear model using the penalty-function approach. The methodology is validated by testing on several simulated and experimental datasets and yields better results than other existing minimum-zone algorithms.

Application of Multi-Objective Evolutionary Algorithms to Antenna and Microwave Design Problems

2012 ◽  
pp. 75-101
Author(s):  
Sotirios K. Goudos
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithms ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Design Problems ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Microwave Design ◽  
Generalized Differential

Antenna and microwave design problems are, in general, multi-objective. Multi-objective Evolutionary Algorithms (MOEAs) are suitable optimization techniques for solving such problems. Particle Swarm Optimization (PSO) and Differential Evolution (DE) have received increased interest from the electromagnetics community. The fact that both algorithms can efficiently handle arbitrary optimization problems has made them popular for solving antenna and microwave design problems. This chapter presents three different state-of-the-art MOEAs based on PSO and DE, namely: the Multi-objective Particle Swarm Optimization (MOPSO), the Multi-objective Particle Swarm Optimization with fitness sharing (MOPSO-fs), and the Generalized Differential Evolution (GDE3). Their applications to different design cases from antenna and microwave problems are reported. These include microwave absorber, microwave filters and Yagi-uda antenna design. The algorithms are compared and evaluated against other evolutionary multi-objective algorithms like Nondominated Sorting Genetic Algorithm-II (NSGA-II). The results show the advantages of using each algorithm.

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