scholarly journals A Swarm Optimization Genetic Algorithm Based on Quantum-Behaved Particle Swarm Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-15 ◽  
Author(s):  
Tao Sun ◽  
Ming-hai Xu
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Search Space ◽  
Particle Movement ◽  
Swarm Optimization ◽  
Search Spaces ◽  
Dimension Functions ◽  
Solution Accuracy ◽  
Crossover And Mutation

Quantum-behaved particle swarm optimization (QPSO) algorithm is a variant of the traditional particle swarm optimization (PSO). The QPSO that was originally developed for continuous search spaces outperforms the traditional PSO in search ability. This paper analyzes the main factors that impact the search ability of QPSO and converts the particle movement formula to the mutation condition by introducing the rejection region, thus proposing a new binary algorithm, named swarm optimization genetic algorithm (SOGA), because it is more like genetic algorithm (GA) than PSO in form. SOGA has crossover and mutation operator as GA but does not need to set the crossover and mutation probability, so it has fewer parameters to control. The proposed algorithm was tested with several nonlinear high-dimension functions in the binary search space, and the results were compared with those from BPSO, BQPSO, and GA. The experimental results show that SOGA is distinctly superior to the other three algorithms in terms of solution accuracy and convergence.

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.

A Condensed Hybrid Optimization Algorithm Using Enhanced Continuous Tabu Search and Particle Swarm Optimization

2009 ◽  
Author(s):  
Cheng-Hung Chen ◽  
Marco P. Schoen ◽  
Ken W. Bosworth
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Particle Swarm ◽  
Search Space ◽  
Hybrid Optimization ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Search Spaces ◽  
Hybrid Optimization Algorithm ◽  
Promising Area

A novel Condensed Hybrid Optimization (CHO) algorithm using Enhanced Continuous Tabu Search (ECTS) and Particle Swarm Optimization (PSO) is proposed. The proposed CHO algorithm combines the respective strengths of ECTS and PSO. The ECTS is a modified Tabu Search (TS), which has good search capabilities on large search spaces. In this study, ECTS is utilized to define smaller search spaces, which are used in a second stage by the basic PSO to find the respective local optimum. The ECTS covers the global search space by using a TS concept called diversification and then selects the most promising areas in the search space. Once the promising regions in the search space are defined, the proposed CHO algorithm employs another TS concept called intensification in order to search the promising area thoroughly. The proposed CHO algorithm is tested with the multi-dimensional Hyperbolic and Rosenbrock problems. Compared to other four algorithms, the simulations results indicate that the accuracy and effectiveness of the proposed CHO algorithm.

A New Hybrid Genetic Algorithm and Particle Swarm Optimization

2012 ◽  
Vol 498 ◽  
pp. 115-125 ◽  
Author(s):  
H. Hachimi ◽  
Rachid Ellaia ◽  
A. El Hami
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Hybrid Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Hybrid Technique ◽  
Swarm Optimization ◽  
New Generation ◽  
Crossover And Mutation

In this paper, we present a new hybrid algorithm which is a combination of a hybrid genetic algorithm and particle swarm optimization. We focus in this research on a hybrid method combining two heuristic optimization techniques, genetic algorithms (GA) and particle swarm optimization (PSO) for the global optimization. Denoted asGA-PSO, this hybrid technique incorporates concepts fromGAandPSOand creates individuals in a new generation not only by crossover and mutation operations as found inGAbut also by mechanisms ofPSO. The performance of the two algorithms has been evaluated using several experiments.

scholarly journals A comparative study of particle swarm optimization and genetic algorithm

2019 ◽  
Vol 8 (2) ◽  
pp. 40
Author(s):  
Saman M. Almufti ◽  
Amar Yahya Zebari ◽  
Herman Khalid Omer
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Comparative Study ◽  
Evolutionary Computation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Computation Algorithms ◽  
Crossover And Mutation

This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation.  

Adaptive Neuro-Fuzzy Control Approach Based on Particle Swarm Optimization

2012 ◽  
pp. 81-98
Author(s):  
Gomaa Zaki El-Far
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Fuzzy Controller ◽  
Particle Swarm ◽  
Search Space ◽  
Swarm Optimization ◽  
Control Approach ◽  
Search Spaces ◽  
Neuro Fuzzy ◽  
Non Linear

This paper proposes a modified particle swarm optimization algorithm (MPSO) to design adaptive neuro-fuzzy controller parameters for controlling the behavior of non-linear dynamical systems. The modification of the proposed algorithm includes adding adaptive weights to the swarm optimization algorithm, which introduces a new update. The proposed MPSO algorithm uses a minimum velocity threshold to control the velocity of the particles, avoids clustering of the particles, and maintains the diversity of the population in the search space. The mechanism of MPSO has better potential to explore good solutions in new search spaces. The proposed MPSO algorithm is also used to tune and optimize the controller parameters like the scaling factors, the membership functions, and the rule base. To illustrate the adaptation process, the proposed neuro-fuzzy controller based on MPSO algorithm is applied successfully to control the behavior of both non-linear single machine power systems and non-linear inverted pendulum systems. Simulation results demonstrate that the adaptive neuro-fuzzy logic controller application based on MPSO can effectively and robustly enhance the damping of oscillations.

scholarly journals Memes Evolution in a Memetic Variant of Particle Swarm Optimization

Mathematics ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 423 ◽  
Author(s):  
Umberto Bartoccini ◽  
Arturo Carpi ◽  
Valentina Poggioni ◽  
Valentino Santucci
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Particle Swarm ◽  
Search Space ◽  
Benchmark Problems ◽  
Hybrid Search ◽  
Swarm Optimization ◽  
Search Spaces ◽  
Starting Point ◽  
Adaptive Pso

In this work, a coevolving memetic particle swarm optimization (CoMPSO) algorithm is presented. CoMPSO introduces the memetic evolution of local search operators in particle swarm optimization (PSO) continuous/discrete hybrid search spaces. The proposed solution allows one to overcome the rigidity of uniform local search strategies when applied to PSO. The key contribution is that memes provides each particle of a PSO scheme with the ability to adapt its exploration dynamics to the local characteristics of the search space landscape. The objective is obtained by an original hybrid continuous/discrete meme representation and a probabilistic co-evolving PSO scheme for discrete, continuous, or hybrid spaces. The coevolving memetic PSO evolves both the solutions and their associated memes, i.e. the local search operators. The proposed CoMPSO approach has been experimented on a standard suite of numerical optimization benchmark problems. Preliminary experimental results show that CoMPSO is competitive with respect to standard PSO and other memetic PSO schemes in literature, and its a promising starting point for further research in adaptive PSO local search operators.

Genetic algorithm search space splicing particle swarm optimization as general-purpose optimizer

2013 ◽  
Vol 128 ◽  
pp. 153-159 ◽  
Author(s):  
Hao Li ◽  
Chanin Nantasenamat ◽  
Teerawat Monnor ◽  
Chartchalerm Isarankura-Na-Ayudhya ◽  
Virapong Prachayasittikul
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Search Space ◽  
General Purpose ◽  
Swarm Optimization ◽  
Algorithm Search

scholarly journals An Expanded Heterogeneous Particle Swarm Optimization Based on Adaptive Inertia Weight

2021 ◽  
Vol 2021 ◽  
pp. 1-24
Author(s):  
Sami Zdiri ◽  
Jaouher Chrouta ◽  
Abderrahmen Zaafouri
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Small Region ◽  
Local Optimum ◽  
Swarm Optimization ◽  
Search Spaces ◽  
Inertia Weight ◽  
Solution Accuracy

In this study, a modified version of multiswarm particle swarm optimization algorithm (MsPSO) is proposed. However, the classical MsPSO algorithm causes premature stagnation due to the limitation of particle diversity; as a result, it is simple to slip into a local optimum. To overcome the above feebleness, this work presents a heterogeneous multiswarm PSO algorithm based on adaptive inertia weight strategies called (A-MsPSO). The MsPSO’s main advantages are that it is simple to use and that there are few settings to alter. In the MsPSO method, the inertia weight is a key parameter affecting considerably convergence, exploration, and exploitation. In this manuscript, an adaptive inertia weight is adopted to ameliorate the global search ability of the classical MsPSO algorithm. Its performance is based on exploration, which is defined as an algorithm’s capacity to search through a variety of search spaces. It also aids in determining the best ideal capability for searching a small region and determining the candidate answer. If a swarm discovers a global best location during iterations, the inertia weight is increased, and exploration in that direction is enhanced. The standard tests and indications provided in the specialized literature are used to show the efficiency of the proposed algorithm. Furthermore, findings of comparisons between A-MsPSO and six other common PSO algorithms show that our proposal has a highly promising performance for handling various types of optimization problems, leading to both greater solution accuracy and more efficient solution times.

A Comparative Study of Particle Swarm Optimization and Genetic Algorithm

2020 ◽  
Vol 1 (1) ◽  
pp. 33-45
Author(s):  
Nazrin Hasanova
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Comparative Study ◽  
Evolutionary Computation ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Computation Algorithms ◽  
Crossover And Mutation

This paper provides an introduction and a comparison of two widely used evolutionary computation algorithms:  Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) based on the previous studies and researches. It describes Genetic Algorithm basic functionalities including various steps such as selection, crossover, and mutation.

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