Opposition-Based Particle Swarm Optimization with Velocity Clamping (OVCPSO)

Shahzad, Farrukh; Baig, Abdul Rauf; Masood, Sohail; Kamran, Muhammad; Naveed, Nawazish

doi:10.1007/978-3-642-03156-4_34

Cited by 40 publications

(13 citation statements)

References 8 publications

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“…Equations (12), (18), and (19) can be combined to calculate the probability of the quasi-reflected point being closer than the EA individual to the solution…”

Section: Conditional Probability Of Quasi-reflection Versus Ea Indivimentioning

confidence: 99%

Mathematical and Experimental Analyses of Oppositional Algorithms

Ergezer

Simon

2014

IEEE Trans. Cybern.

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Evolutionary algorithms (EAs) are widely employed for solving optimization problems with rugged fitness landscapes. Opposition-based learning (OBL) is a recent tool developed to improve the convergence rate of EAs. In this paper, we derive the probabilities that distances between OBL points and the optimization problem solution are less than the distance between a given EA individual and the optimal solution. We find that the quasi-reflected opposition point yields the highest probability and is the most likely candidate to be closer to the optimal solution. We then employ CEC 2013 competition benchmark problems and select a set of trajectory optimization problems from the European Space Agency to study the performance of three OBL algorithms in conjunction with three different EAs. The CEC 2013 test suit simulations indicate that quasi-reflection accelerates the performance of the EA, especially for more difficult composition functions. The space trajectory experiments reveal that differential evolution with opposition generally returns the best objective function value for the chosen minimization problems.

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“…Equations (12), (18), and (19) can be combined to calculate the probability of the quasi-reflected point being closer than the EA individual to the solution…”

Section: Conditional Probability Of Quasi-reflection Versus Ea Indivimentioning

confidence: 99%

Mathematical and Experimental Analyses of Oppositional Algorithms

Ergezer

Simon

2014

IEEE Trans. Cybern.

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“…A variant of P SO that uses velocity clamping (V CP SO) was extended by using OBL (OV CP SO) [45]. Opposition was employed during the initialization and iteration phases.…”

Section: Optimizationmentioning

confidence: 99%

Opposition based computing — A survey

Al-Qunaieer

Tizhoosh

Rahnamayan

2010

The 2010 International Joint Conference on Neural Networks (IJCNN)

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In algorithms design, one of the important aspects is to consider efficiency. Many algorithm design paradigms are existed and used in order to enhance algorithms' efficiency. Opposition-based Learning (OBL) paradigm was recently introduced as a new way of thinking during the design of algorithms. The concepts of opposition have already been used and applied in several applications. These applications are from different fields, such as optimization algorithms, learning algorithms and fuzzy logic. The reported results confirm that OBL paradigm was promising to accelerate or to enhance accuracy of soft computing algorithms. In this paper, a survey of existing applications of opposition-based computing is presented.

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“…There are some researchers that have develop velocity clamping method, such as : [5], [6] b. Inertia weight It is a mechanism to control an exploration and exploitation abilities of the swarm, and as mechanism to eliminate the need of velocity clamping. The inertia weight, , controls the momentum of the particle by weighing the contribution of the previous velocity -basically controlling how much memory of the previous flight direction will influence the new velocity.…”

Section: Basic Variants Of Psomentioning

confidence: 99%

“…The inertia weight, , controls the momentum of the particle by weighing the contribution of the previous velocity -basically controlling how much memory of the previous flight direction will influence the new velocity. For the PSO, the velocity equation [5] changes from equation:…”

Section: Basic Variants Of Psomentioning

confidence: 99%

Particle Swarm Optimization: Technique, System and Challenges

Rini¹,

Shamsuddin²,

Yuhaniz³

2011

IJCA

202

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Particle Swarm Optimization (PSO) is a biologically inspired computational search and optimization method developed in 1995 by Eberhart and Kennedy based on the social behaviors of birds flocking or fish schooling. A number of basic variations have been developed due to improve speed of convergence and quality of solution found by the PSO. On the other hand, basic PSO is more appropriate to process static, simple optimization problem. Modification PSO is developed for solving the basic PSO problem. The observation and review 46 related studies in the period between 2002 and 2010 focusing on function of PSO, advantages and disadvantages of PSO, the basic variant of PSO, Modification of PSO and applications that have implemented using PSO. The application can show which one the modified or variant PSO that haven't been made and which one the modified or variant PSO that will be developed.

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Opposition-Based Particle Swarm Optimization with Velocity Clamping (OVCPSO)

Cited by 40 publications

References 8 publications

Mathematical and Experimental Analyses of Oppositional Algorithms

Mathematical and Experimental Analyses of Oppositional Algorithms

Opposition based computing — A survey

Particle Swarm Optimization: Technique, System and Challenges

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Opposition-Based Particle Swarm Optimization with Velocity Clamping (OVCPSO)

Cited by 40 publications

References 8 publications

Mathematical and Experimental Analyses of Oppositional Algorithms

Mathematical and Experimental Analyses of Oppositional Algorithms

Opposition based computing &#x2014; A survey

Particle Swarm Optimization: Technique, System and Challenges

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Opposition based computing — A survey