Comparative Study of Path Planning by Particle Swarm Optimization and Genetic Algorithm

2014 ◽  
Vol 687-691 ◽  
pp. 1420-1424
Author(s):  
Hai Tao Han ◽  
Wan Feng Ji ◽  
Yao Qing Zhang ◽  
De Peng Sha
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Performance ◽  
Operational Environment ◽  
Swarm Optimization ◽  
Vehicle Path ◽  
Swarm Intelligence Algorithm

Two main requirements of the optimization problems are included: one is finding the global minimum, the other is obtaining fast convergence speed. As heuristic algorithm and swarm intelligence algorithm, both particle swarm optimization and genetic algorithm are widely used in vehicle path planning because of their favorable search performance. This paper analyzes the characteristics and the same and different points of two algorithms as well as making simulation experiment under the same operational environment and threat states space. The result shows that particle swarm optimization is superior to genetic algorithm in searching speed and convergence.

Vehicle Path Planning Method Based on Particle Swarm Optimization Algorithm

2012 ◽  
Vol 468-471 ◽  
pp. 2745-2748
Author(s):  
Sheng Long Yu ◽  
Yu Ming Bo ◽  
Zhi Min Chen ◽  
Kai Zhu
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Path Planning ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Adaptive Genetic Algorithm ◽  
Swarm Optimization ◽  
Vehicle Path

A particle swarm optimization algorithm (PSO) is presented for vehicle path planning in the paper. Particle swarm optimization proposed by Kennedy and Eberhart is derived from the social behavior of the birds foraging. Particle swarm optimization algorithm a kind of swarm-based optimization method.The simulation experiments performed in this study show the better vehicle path planning ability of PSO than that of adaptive genetic algorithm and genetic algorithm. The experimental results show that the vehicle path planning by using PSO algorithm has the least cost and it is indicated that PSO algorithm has more excellent vehicle path planning ability than adaptive genetic algorithm,genetic algorithm.

scholarly journals A Modified NM-PSO Method for Parameter Estimation Problems of Models

2012 ◽  
Vol 2012 ◽  
pp. 1-12 ◽  
Author(s):  
An Liu ◽  
Erwie Zahara ◽  
Ming-Ta Yang
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Differential Equations ◽  
Ordinary Differential Equations ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Wide Range ◽  
Estimation Problems

Ordinary differential equations usefully describe the behavior of a wide range of dynamic physical systems. The particle swarm optimization (PSO) method has been considered an effective tool for solving the engineering optimization problems for ordinary differential equations. This paper proposes a modified hybrid Nelder-Mead simplex search and particle swarm optimization (M-NM-PSO) method for solving parameter estimation problems. The M-NM-PSO method improves the efficiency of the PSO method and the conventional NM-PSO method by rapid convergence and better objective function value. Studies are made for three well-known cases, and the solutions of the M-NM-PSO method are compared with those by other methods published in the literature. The results demonstrate that the proposed M-NM-PSO method yields better estimation results than those obtained by the genetic algorithm, the modified genetic algorithm (real-coded GA (RCGA)), the conventional particle swarm optimization (PSO) method, and the conventional NM-PSO method.

Mathematical Optimization by Using Particle Swarm Optimization, Genetic Algorithm, and Differential Evolution and Its Similarities

2021 ◽  
pp. 1-34
Author(s):  
Shailendra Aote ◽  
Mukesh M. Raghuwanshi
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Mathematical Optimization ◽  
Evolutionary Computing ◽  
Swarm Optimization

To solve the problems of optimization, various methods are provided in different domain. Evolutionary computing (EC) is one of the methods to solve these problems. Mostly used EC techniques are available like Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolution (DE). These techniques have different working structure but the inner working structure is same. Different names and formulae are given for different task but ultimately all do the same. Here we tried to find out the similarities among these techniques and give the working structure in each step. All the steps are provided with proper example and code written in MATLAB, for better understanding. Here we started our discussion with introduction about optimization and solution to optimization problems by PSO, GA and DE. Finally, we have given brief comparison of these.

scholarly journals Large-Scale Complex Network Community Detection Combined with Local Search and Genetic Algorithm

2020 ◽  
Vol 10 (9) ◽  
pp. 3126
Author(s):  
Desheng Lyu ◽  
Bei Wang ◽  
Weizhe Zhang
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Community Structure ◽  
Local Search ◽  
Complex Networks ◽  
Community Detection ◽  
Large Scale ◽  
Particle Swarm ◽  
Search Performance ◽  
Swarm Optimization

With the development of network technology and the continuous advancement of society, the combination of various industries and the Internet has produced many large-scale complex networks. A common feature of complex networks is the community structure, which divides the network into clusters with tight internal connections and loose external connections. The community structure reveals the important structure and topological characteristics of the network. The detection of the community structure plays an important role in social network analysis and information recommendation. Therefore, based on the relevant theory of complex networks, this paper introduces several common community detection algorithms, analyzes the principles of particle swarm optimization (PSO) and genetic algorithm and proposes a particle swarm-genetic algorithm based on the hybrid algorithm strategy. According to the test function, the single and the proposed algorithm are tested, respectively. The results show that the algorithm can maintain the good local search performance of the particle swarm optimization algorithm and also utilizes the good global search ability of the genetic algorithm (GA) and has good algorithm performance. Experiments on each community detection algorithm on real network and artificially generated network data sets show that the particle swarm-genetic algorithm has better efficiency in large-scale complex real networks or artificially generated networks.

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.

Concurrent Societies Based on Genetic Algorithm and Particle Swarm Optimization

2010 ◽  
Vol 14 (1) ◽  
pp. 110-118
Author(s):  
Hrvoje Markovic ◽  
◽  
Fangyan Dong ◽  
Kaoru Hirota
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Nearest Neighbor ◽  
Optimization Problems ◽  
Fitness Function ◽  
Particle Swarm ◽  
Absolute Error ◽  
Population Based ◽  
Test Problems ◽  
Swarm Optimization

A parallel multi-population based metaheuristic optimization framework, called Concurrent Societies, inspired by human intellectual evolution, is proposed. It uses population based metaheuristics to evolve its populations, and fitness function approximations as representations of knowledge. By utilizing iteratively refined approximations it reduces the number of required evaluations and, as a byproduct, it produces models of the fitness function. The proposed framework is implemented as two Concurrent Societies: one based on genetic algorithm and one based on particle swarm optimization both using k -nearest neighbor regression as fitness approximation. The performance is evaluated on 10 standard test problems and compared to other commonly used metaheuristics. Results show that the usage of the framework considerably increases efficiency (by a factor of 7.6 to 977) and effectiveness (absolute error reduced by more than few orders of magnitude). The proposed framework is intended for optimization problems with expensive fitness functions, such as optimization in design and interactive optimization.

Evolving Particle Swarm Optimization Implemented by a Genetic Algorithm

2008 ◽  
Vol 12 (3) ◽  
pp. 284-289 ◽  
Author(s):  
Jenn-Long Liu ◽  
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Social Learning ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Space ◽  
Design Parameters ◽  
Search Process ◽  
Swarm Optimization ◽  
Learning Rates

Particle swarm optimization (PSO) is a promising evolutionary approach related to a particle moves over the search space with velocity, which is adjusted according to the flying experiences of the particle and its neighbors, and flies towards the better and better search area over the course of search process. Although the PSO is effective in solving the global optimization problems, there are some crucial user-input parameters, such as cognitive and social learning rates, affect the performance of algorithm since the search process of a PSO algorithm is nonlinear and complex. Consequently, a PSO with well-selected parameter settings may result in good performance. This work develops an evolving PSO based on the Clerc’s PSO to evaluate the fitness of objective function and a genetic algorithm (GA) to evolve the optimal design parameters to provide the usage of PSO. The crucial design parameters studied herein include the cognitive and social learning rates as well as constriction factor for the Clerc’s PSO. Several benchmarking cases are experimented to generalize a set of optimal parameters via the evolving PSO. Furthermore, the better parameters are applied to the engineering optimization of a pressure vessel design.

Particle Swarm Optimization by Improved Chaos Immune Genetic Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 2621-2624
Author(s):  
Shao Song Wan ◽  
Jian Cao ◽  
Qun Song Zhu
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Single Chip ◽  
Immune Genetic Algorithm ◽  
Swarm Optimization ◽  
Local Optima ◽  
Chaos Sequence ◽  
Multimodal Problems

In order to resolve these problems, we put forward a new design of the intelligent lock which is mainly based on the technology of wireless sensor network. Particle swarm optimization (PSO) is a recently proposed intelligent algorithm which is motivated by swarm intelligence. PSO has been shown to perform well on many benchmark and real-world optimization problems; it easily falls into local optima when solving complex multimodal problems. To avoid the local optimization, the algorithm renews population and enhances the diversity of population by using density calculation of immune theory and adjusting new chaos sequence. The paper gives the circuit diagram of the hardware components based on single chip and describe how to design the software. The experimental results show that the immune genetic algorithm based on chaos theory can search the result of the optimization and evidently improve the convergent speed and astringency.

scholarly journals Adaptive Multiswarm Comprehensive Learning Particle Swarm Optimization

Information ◽  
2018 ◽  
Vol 9 (7) ◽  
pp. 173 ◽  
Author(s):  
Xiang Yu ◽  
Claudio Estevez
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Velocity ◽  
Pareto Front ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Search Performance ◽  
Swarm Optimization ◽  
Nondominated Solutions ◽  
The Difference ◽  
Update Strategy

Multiswarm comprehensive learning particle swarm optimization (MSCLPSO) is a multiobjective metaheuristic recently proposed by the authors. MSCLPSO uses multiple swarms of particles and externally stores elitists that are nondominated solutions found so far. MSCLPSO can approximate the true Pareto front in one single run; however, it requires a large number of generations to converge, because each swarm only optimizes the associated objective and does not learn from any search experience outside the swarm. In this paper, we propose an adaptive particle velocity update strategy for MSCLPSO to improve the search efficiency. Based on whether the elitists are indifferent or complex on each dimension, each particle adaptively determines whether to just learn from some particle in the same swarm, or additionally from the difference of some pair of elitists for the velocity update on that dimension, trying to achieve a tradeoff between optimizing the associated objective and exploring diverse regions of the Pareto set. Experimental results on various two-objective and three-objective benchmark optimization problems with different dimensional complexity characteristics demonstrate that the adaptive particle velocity update strategy improves the search performance of MSCLPSO significantly and is able to help MSCLPSO locate the true Pareto front more quickly and obtain better distributed nondominated solutions over the entire Pareto front.

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