Rank Based Particle Swarm Optimization

Khan, Anwarzeb; Sadeeq-Ullah, M.; Riaz-ul-Hasnain, Riaz-ul-Hasnain; Azzam-ul-Asar, Azzam-ul-Asar

doi:10.1007/978-3-642-15461-4_24

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…This evolutionary computational method was first invented by Kennedy and Eberhart (1995) and Kennedy (1997) in the mid 1990s on graceful collaborative motion of biological populations rooted on the concept of “information sharing and collective intelligence.” This adaptive metahurestic technique emphasizes on overcoming the energy barriers, particularly by the upgradation of positions and velocities following the individual or personal best which again follows the global best one. After several developments (Reeves, 1983; Reynolds, 1987; Heppner and Grenander, 1990; Millonas, 1993; Clerc, 1999; Eberhart and Shi, 2000; Banks et al, 2007; Bui et al, 2007; Khan and Sadeequllah, 2010), adaptation (Wang et al, 2011), modifications (like niching with PSO Brits et al, 2002; Engelbrecht and Van Loggerenberg, 2007; Sun et al, 2007; Nickabadi et al, 2008; Wang J. et al, 2009; Wang Y. et al, 2009) single solution PSO (Liu and Wang, 2006; AlRashidi and El-Hawary, 2007; Li and Li, 2007; Liu B. et al, 2007; Liu D. et al, 2007; Petalas et al, 2007; Schutze et al, 2007; Zhang et al, 2007; Zhang and Wang, 2008; Benameur et al, 2009) and multi-objective optimization (Cai et al, 2004, 2009; Call et al, 2007; Chandrasekaran et al, 2007; Abido, 2009; Alatas and Akin, 2009; Dehuri and Cho, 2009; De Carvalho et al, 2010; Goh et al, 2010; Briza and Naval, 2011; Chen et al, 2011), constraint optimization with PSO (Cao et al, 2004; AlRashidi and El-Hawary, 2006; Sun and Gao, 2008; Ma et al, 2009; Sivasubramani and Swarup, 2009), discrete PSO (Yin, 2004; Yeh, 2009; Yeh et al, 2009; Unler and Murat, 2010), dynamic environment of PSO (Shao et al, 2004, 2008; Zhang et al, 2006; Chen et al, 2007; Liu X. et al, 2007; Yang et al, 2007; Du and Li, 2008; Wang and Xing, 2008; Zhao et al, 2008; Cheng et al, 2009; Wang Y. et al, 2009; Bae et al, 2010) and pa...…”

Section: Introductionmentioning

confidence: 99%

Modified Particle Swarm Optimization Algorithms for the Generation of Stable Structures of Carbon Clusters, Cn (n = 3–6, 10)

et al. 2019

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Particle Swarm Optimization (PSO), a population based technique for stochastic search in a multidimensional space, has so far been employed successfully for solving a variety of optimization problems including many multifaceted problems, where other popular methods like steepest descent, gradient descent, conjugate gradient, Newton method, etc. do not give satisfactory results. Herein, we propose a modified PSO algorithm for unbiased global minima search by integrating with density functional theory which turns out to be superior to the other evolutionary methods such as simulated annealing, basin hopping and genetic algorithm. The present PSO code combines evolutionary algorithm with a variational optimization technique through interfacing of PSO with the Gaussian software, where the latter is used for single point energy calculation in each iteration step of PSO. Pure carbon and carbon containing systems have been of great interest for several decades due to their important role in the evolution of life as well as wide applications in various research fields. Our study shows how arbitrary and randomly generated small C n clusters ( n = 3–6, 10) can be transformed into the corresponding global minimum structure. The detailed results signify that the proposed technique is quite promising in finding the best global solution for small population size clusters.

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Section: Introductionmentioning

confidence: 99%

Modified Particle Swarm Optimization Algorithms for the Generation of Stable Structures of Carbon Clusters, Cn (n = 3–6, 10)

et al. 2019

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“…Furthermore, several developments, adaptation and modifications on the standard PSO (like single solution PSO, niching with PSO, and constraint optimization with PSO, multi objective optimization, dynamic environment of PSO, discrete PSO as well as parameterization of the original PSO are carried out to generate an efficient algorithm to achieve the best solution. Current research is devoted to know the nature of development, including premature convergence, topology, biases, hybridization of PSO algorithm.…”

Section: Introductionmentioning

confidence: 99%

A machine learning technique toward generating minimum energy structures of small boron clusters

Mitikiri

Jana

Sural

et al. 2018

Int J of Quantum Chemistry

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The search for a global minimum related to molecular electronic structure and chemical bonding has received wide attention based on some theoretical calculations at various levels of theory.Particle swarm optimization (PSO) algorithm and modified PSO have been used to predict the energetically stable/metastable states associated with a given chemical composition. Out of a variety of techniques such as genetic algorithm, basin hopping, simulated annealing, PSO, and so on, PSO is considered to be one of the most suitable methods due to its various advantages over others. We use a swarm-intelligence based parallel code to improve a PSO algorithm in a multidimensional search space augmented by quantum chemical calculations on gas phase structures at 0 K without any symmetry constraint to obtain an optimal solution. Our currently employed code is interfaced with Gaussian software for single point energy calculations. The code developed here is shown to be efficient. Small population size (small cluster) in the multidimensional space is actually good enough to get better results with low computational cost than the typical larger population. But for larger systems also the analysis is possible. One can try with a large number of particles as well. We have also analyzed how arbitrary and random structures and the local minimum energy structures gravitate toward the target global minimum structure. At the same time, we compare our results with that obtained from other evolutionary techniques. K E Y W O R D SB 5 and B 6 clusters, global optimal structure, multi-threaded code, optimization, particle swarm optimization algorithm

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A novel approach to solving weapon-target assignment problem based on hybrid particle swarm optimization algorithm

Zhu

Zou

Wei

2011

Proceedings of 2011 International Conference on Electronic &Amp; Mechanical Engineering and Information Technology

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Rank Based Particle Swarm Optimization

Cited by 3 publications

References 2 publications

Modified Particle Swarm Optimization Algorithms for the Generation of Stable Structures of Carbon Clusters, Cn (n = 3–6, 10)

Modified Particle Swarm Optimization Algorithms for the Generation of Stable Structures of Carbon Clusters, Cn (n = 3–6, 10)

A machine learning technique toward generating minimum energy structures of small boron clusters

A novel approach to solving weapon-target assignment problem based on hybrid particle swarm optimization algorithm

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