scholarly journals Improvement of Imperialist Competitive Algorithm based on the Cosine Similarity Criterion of Neighboring Objects

2021 ◽  
Vol 18 (3) ◽  
Author(s):  
Maryam Houtinezhad ◽  
Hamid Reza Ghaffary
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Similarity Criterion ◽  
Imperialist Competitive Algorithm ◽  
Cosine Similarity ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Competitive Algorithm ◽  
Combined Algorithm

The goal of optimizing the best acceptable answer is according to the limitations and needs of the problem. For a problem, there are several different answers that are defined to compare them and select an optimal answer; a function is called a target function. The choice of this function depends on the nature of the problem. Sometimes several goals are together optimized; such optimization problems are called multi-objective issues. One way to deal with such problems is to form a new objective function in the form of a linear combination of the main objective functions. In the proposed approach, in order to increase the ability to discover new position in the Imperialist Competitive Algorithm (ICA), its operators are combined with the particle swarm optimization. The colonial competition optimization algorithm has the ability to search global and has a fast convergence rate, and the particle swarm algorithm added to it increases the accuracy of searches. In this approach, the cosine similarity of the neighboring countries is measured by the nearest colonies of an imperialist and closest competitor country. In the proposed method, by balancing the global and local search, a method for improving the performance of the two algorithms is presented. The simulation results of the combined algorithm have been evaluated with some of the benchmark functions. Comparison of the results has been evaluated with respect to metaheuristic algorithms such as Differential Evolution (DE), Ant Lion Optimizer (ALO), ICA, Particle Swarm Optimization (PSO), and Genetic Algorithm (GA).

Research on an Improved Coordinating Method Based on Genetic Algorithms and Particle Swarm Optimization

2019 ◽  
Vol 13 (2) ◽  
pp. 18-29
Author(s):  
Rongrong Li ◽  
Linrun Qiu ◽  
Dongbo Zhang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Bottom Layer ◽  
Pso Algorithm ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Elite Group ◽  
Searching Ability

In this article, a hierarchical cooperative algorithm based on the genetic algorithm and the particle swarm optimization is proposed that the paper should utilize the global searching ability of genetic algorithm and the fast convergence speed of particle swarm optimization. The proposed algorithm starts from Individual organizational structure of subgroups and takes full advantage of the merits of the particle swarm optimization algorithm and the genetic algorithm (HCGA-PSO). The algorithm uses a layered structure with two layers. The bottom layer is composed of a series of genetic algorithm by subgroup that contributes to the global searching ability of the algorithm. The upper layer is an elite group consisting of the best individuals of each subgroup and the particle swarm algorithm is used to perform precise local search. The experimental results demonstrate that the HCGA-PSO algorithm has better convergence and stronger continuous search capability, which makes it suitable for solving complex optimization problems.

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.

Aerodynamic Shape Optimization Using Improved Territorial Particle Swarm Algorithm

2012 ◽  
Author(s):  
Amir Nejat ◽  
Pooya Mirzabeygi ◽  
Masoud Shariat-Panahi
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Weighted Average ◽  
Particle Swarm Algorithm ◽  
Density Estimator ◽  
Objective Functions ◽  
Swarm Optimization ◽  
Multi Objective

In this paper, a new robust optimization technique with the ability of solving single and multi-objective constrained design optimization problems in aerodynamics is presented. This new technique is an improved Territorial Particle Swarm Optimization (TPSO) algorithm in which diversity is actively preserved by avoiding overcrowded clusters of particles and encouraging broader exploration. Adaptively varying “territories” are formed around promising individuals to prevent many of the lesser individuals from premature clustering and encouraged them to explore new neighborhoods based on a hybrid self-social metric. Also, a new social interaction scheme is introduced which guided particles towards the weighted average of their “elite” neighbors’ best found positions instead of their own personal bests which in turn helps the particles to exploit the candidate local optima more effectively. The TPSO algorithm is developed to take into account multiple objective functions using a Pareto-Based approach. The non-dominated solutions found by swarm are stored in an external archive and nearest neighbor density estimator method is used to select a leader for the individual particles in the swarm. Efficiency and robustness of the proposed algorithm is demonstrated using multiple traditional and newly-composed optimization benchmark functions and aerodynamic design problems. In final airfoil design obtained from the Multi Objective Territorial Particle Swarm Optimization algorithm, separation point is delayed to make the airfoil less susceptible to stall in high angle of attack conditions. The optimized airfoil also reveals an evident improvement over the test case airfoil across all objective functions presented.

Solving Nonlinear Optimization Problems of Steering Trapezoid Mechanism Based on an IPSO

2014 ◽  
Vol 945-949 ◽  
pp. 607-613
Author(s):  
Ling Liu ◽  
Pei Zhou ◽  
Jun Luo ◽  
Zan Pi
Keyword(s):  
Particle Swarm Optimization ◽  
Simulated Annealing ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Steering Wheel ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Rotational Angle ◽  
Improved Particle Swarm Optimization

The paper focus on an improved particle swarm optimization (IPSO) used to solve nonlinear optimization problems of steering trapezoid mechanism. First of all, nonlinear optimization model of steering trapezoid mechanism is established. Sum of absolute value of difference between actual rotational angle of anterolateral steering wheel and theoretical rotational angle of anterolateral steering wheel is taken as objective function, bottom angle and steering arm length of steering trapezoid mechanism are selected to be design variables. After that, an improved particle swarm optimization algorithm is proposed by introducing Over-flow exception dealing functions to deal with complicated nonlinear constraints. Finally, codes for IPSO are programmed and parameters of steering trapezoid mechanism for different models are optimized, and numerical result shows that errors of objective function's ideal values and objective function's optimization values are minimal. Performance comparison experiment of different intelligent algorithms indicates that the proposed new algorithm is superior to Particle swarm algorithm based on simulated annealing (SA-PSO) and traditional particle swarm optimization (TPSO) in good and fast convergence and small calculating quantity, but a little inferior to particle swarm algorithm based on simulated annealing (SA-PSO) in calculation accuracy in the process of optimization.

An Enhanced Binary Particle Swarm Optimization for Structural Topology Optimization

2010 ◽  
Vol 224 (10) ◽  
pp. 2271-2287 ◽  
Author(s):  
K-Y Tseng ◽  
C-B Zhang ◽  
C-Y Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Topology Optimization ◽  
Optimization Problems ◽  
Minimum Weight ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
Binary Particle Swarm Optimization ◽  
Structural Topology Optimization ◽  
Structural Topology ◽  
Swarm Optimization

Particle swarm optimization (PSO), a heuristic optimization method, has been successfully applied in solving many optimization problems in real-value search space. The original binary particle swarm optimization (BPSO) uses the concept of bit flipping of the binary string to convert the velocity from a real code into a binary code. However, the conversion process cannot be reversed, and it is difficult to extend this framework to solve certain discrete optimization problems. An enhanced binary particle swarm algorithm is proposed in this study based on pure binary bit-string frameworks to deal with structural topology optimization problems. Further, two enhancement strategies, stress-based strategy and pair-switched strategy, were developed to improve the performance of the proposed algorithm for topology optimization of structure. The results of experimental cases demonstrated in this study show that the proposed enhanced binary particle swarm optimization (EBPSO) with two developed strategies is an efficient population-based approach for finding the optimal design for structural topology optimization problems of minimum compliance design and minimum weight design.

scholarly journals Multiswarm Particle Swarm Optimization with Transfer of the Best Particle

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Xiao-peng Wei ◽  
Jian-xia Zhang ◽  
Dong-sheng Zhou ◽  
Qiang Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Standard Test ◽  
Engineering Problem ◽  
Particle Swarm Algorithm ◽  
Test Functions ◽  
Swarm Optimization ◽  
Local Optima

We propose an improved algorithm, for a multiswarm particle swarm optimization with transfer of the best particle called BMPSO. In the proposed algorithm, we introduce parasitism into the standard particle swarm algorithm (PSO) in order to balance exploration and exploitation, as well as enhancing the capacity for global search to solve nonlinear optimization problems. First, the best particle guides other particles to prevent them from being trapped by local optima. We provide a detailed description of BMPSO. We also present a diversity analysis of the proposed BMPSO, which is explained based on the Sphere function. Finally, we tested the performance of the proposed algorithm with six standard test functions and an engineering problem. Compared with some other algorithms, the results showed that the proposed BMPSO performed better when applied to the test functions and the engineering problem. Furthermore, the proposed BMPSO can be applied to other nonlinear optimization problems.

Particle Swarm Optimization Method for Panel Layout

2010 ◽  
Vol 450 ◽  
pp. 308-311 ◽  
Author(s):  
Yu Qing Xu ◽  
Zhi Yang ◽  
Qing Xin Meng
Keyword(s):  
Particle Swarm Optimization ◽  
Nuclear Power ◽  
Optimization Design ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Method ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Operational Conditions ◽  
Frequency Sequence

Panel layout in control room of nuclear power plant requires satisfying layout principles and requirement for multiple operational conditions. Traditional panel layout methods are difficult to integrate these principles; the layout scheme heavily depends on designers’ individual judgments and experience. So the randomness of layout scheme is hard to overcome. Particle swarm optimization is a global efficient solution to complex optimization problems with multi-constraints. First, the operational efficiency is selected as optimization object. Second, objective function is constructed according to the evaluating indexes which include operation frequency, sequence and importance of components. Third, Particle swarm algorithm was used to optimize the layout scheme. A case study of layout optimization design is conducted to illustrate the proposed method.

Optimization Technology of PID Parameter in Control System Based on Improved Particle Swarm Optimization Algorithms

2014 ◽  
Vol 908 ◽  
pp. 547-550
Author(s):  
Tian Shun Huang ◽  
Xiao Qiang Li ◽  
Hong Yun Lian ◽  
Zhi Qiang Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mixed Integer ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Combinatorial Optimization Problems ◽  
Swarm Algorithm

Particle swarm algorithm has been proven to be very good solving many global optimization problems. Firstly we improved particle swarm optimization algorithm, the improved PSO algorithm for continuous optimization problem, in solving the nonlinear combinatorial optimization problems and mixed integer nonlinear optimization problems is very effective. This design adopts the improved particle swarm algorithm to optimize the PID parameters of the control system, and the effectiveness of the improved algorithm is proved by experiment.

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