EPSO: Evolutionary Particle Swarms

Miranda, Vladimiro; Keko, Hrvoje; Jaramillo, Álvaro

doi:10.1007/978-3-540-72377-6_6

Cited by 41 publications

(24 citation statements)

References 10 publications

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“…The performance of other topologies, such as Wheel or Pyramid, varies from problem to problem. Miranda et al (2007) proposed a Stochastic Star topology where a particle is informed by gbest with a predefined probability p. Their experimental results showed that the Stochastic Star topology leads in many cases to better results than the original Star topology. The standard PSO 2007 (Clerc, 2008) regenerates a random permutation of particles before each iteration.…”

Section: Exploitation Of Guidance Informationmentioning

confidence: 99%

“…The literature discloses that the use of a dynamic neighborhood (Miranda et al, 2007;Clerc, 2008;Yin et al, 2010) and the local best solution lbest (Kennedy, 1999;Clerc, 2008) leads to a better performance. These notions create a form of multiple neighborhood search in which the neighboring particles (each maintaining a search trajectory) are selected at random or systematically and the local optimum corresponds to the best solution encountered by the multiple search trajectories.…”

Section: Using Guidance Informationmentioning

confidence: 99%

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A Complementary Cyber Swarm Algorithm

Yin

Glover

Laguna

et al. 2013

Recent Algorithms and Applications in Swarm Intelligence Research

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A recent study (Yin et al., 2010) Standard PSO 2007 method (Clerc, 2008. showed that combining particle swarm optimization (PSO) with the strategies of scatter search (SS) and path relinking (PR) produces a Cyber Swarm Algorithm that creates a more effective form of PSO than methods that do not incorporate such mechanisms. This paper proposes a Complementary Cyber Swarm Algorithm (C/CyberSA) that performs in the same league as the original Cyber Swarm Algorithm but adopts different sets of ideas from the tabu search (TS) and the SS/PR template. The C/CyberSA exploits the guidance information and restriction information produced in the history of swarm search and the manipulation of adaptive memory. Responsive strategies using long term memory and path relinking implementations are proposed that make use of critical events encountered in the search. Experimental results with a large set of challenging test functions show that the C/CyberSA outperforms two recently proposed swarm-based methods by finding more optimal solutions while simultaneously using a smaller number of function evaluations. The C/CyberSA approach further produces improvements comparable to those obtained by the original CyberSA in relation to thewere previously found as a basis for focusing the search in regions anticipated to harbor additional solutions of high quality. Diversification promotes the exploration of regions appreciably different from those previously examined in order to produce new solutions with characteristics that depart from those already seen. Intensification and diversification work together to identify new promising regions when the slave heuristics stagnate in the executed search courses. Many intelligent algorithms fall in the territory of metaheuristics. DOI: 10.4018/jsir.2011040102 International Journal of Swarm Intelligence Research, 2(2), 22-41, April-June 2011 23 Copyright © 2011, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.Some exemplary algorithms (Luke, 2009) are genetic algorithms (GA), simulated annealing (SA), ant colony optimization (ACO), tabu search (TS), particle swarm optimization (PSO), scatter search (SS), greedy randomized adaptive search procedure (GRASP), variable neighborhood search (VNS), to name a few. A recent survey and descriptive analysis of metaheuristic algorithms can be found in Sorensen and Glover (2010).Slave heuristics embedded in metaheuristic methods often adopt solution combination or neighborhood exploration processes to generate new solutions based on the current state of search. Solution combination approaches produce new solutions by exchanging information between candidate solutions (for example, crossover operation executed in GA) or by using candidate solutions as guiding points for producing new solutions (for example, by reference to the best experiences in PSO or the path relinking (PR) process used in SS). Alternatively, neighborhood exploration employs incremental changes, called ...

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Section: Exploitation Of Guidance Informationmentioning

confidence: 99%

Section: Using Guidance Informationmentioning

confidence: 99%

A Complementary Cyber Swarm Algorithm

Yin

Glover

Laguna

et al. 2013

Recent Algorithms and Applications in Swarm Intelligence Research

View full text Add to dashboard Cite

show abstract

“…The structure of the EPSO algorithm is summarized below. can be found in [18] and [19]. In brief, in the EPSO algorithm each particle in the swarm evolves from iteration k to iteration k+1 according to expressions (1) and (2).…”

Section: Evolutionary Particle Swarm Optimization Epsomentioning

confidence: 99%

Transmission Expansion Planning – A Multiyear Dynamic Approach Using a Discrete Evolutionary Particle Swarm Optimization Algorithm

Rocha

Saraiva

2012

EPJ Web of Conferences

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Abstract. The basic objective of Transmission Expansion Planning (TEP) is to schedule a number of transmission projects along an extended planning horizon minimizing the network construction and operational costs while satisfying the requirement of delivering power safely and reliably to load centres along the horizon. This principle is quite simple, but the complexity of the problem and the impact on society transforms TEP on a challenging issue. This paper describes a new approach to solve the dynamic TEP problem, based on an improved discrete integer version of the Evolutionary Particle Swarm Optimization (EPSO) meta-heuristic algorithm. The paper includes sections describing in detail the EPSO enhanced approach, the mathematical formulation of the TEP problem, including the objective function and the constraints, and a section devoted to the application of the developed approach to this problem. Finally, the use of the developed approach is illustrated using a case study based on the IEEE 24 bus 38 branch test system.

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“…This stochastic algorithm was developed by James Kennedy and Russell Eberthart in 1995 and modelled 46 Y. Lu et al on the behaviour of a school of fish or a flock of birds (Miranda, Keko, and Jaramillo 2007;Poli, Kennedy, and Blackwell 2007). It is usually used for the optimisation of continuous nonlinear systems.…”

Section: Introductionmentioning

confidence: 99%

An evolutionary performance-enhanced PSO approach by using a BP neural-learning-based PID controller

Levy

Zhang

et al. 2011

Connection Science

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In this paper, a novel evolutionary strategy-based particle swarm optimisation (PSO) approach is presented. The evolutionary strategy is dependent on a BP neural proportional integral derivative (PID) controller, which is intentionally placed between the position and the global best position in a closed loop of their relationship. The BP neural PID controller is designed to aid the position to well track the global best position. Furthermore, it can be utilised to improve the evolutionary dynamics of the PSO algorithm. The experiments for performance evaluation are tried on both analytical benchmark functions and an automatic control system of pulp consistency in pulping and paper-making engineering. The experimental results illustrate that the proposed approach enhances the diversity of swarms, considerably improves the global convergence efficiency and outperforms the PSO algorithm. In addition, it is also easily used in real industrial practice and offers a novel and convenient solution to engineering optimisation design of industrial systems.

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EPSO: Evolutionary Particle Swarms

Cited by 41 publications

References 10 publications

A Complementary Cyber Swarm Algorithm

A Complementary Cyber Swarm Algorithm

Transmission Expansion Planning – A Multiyear Dynamic Approach Using a Discrete Evolutionary Particle Swarm Optimization Algorithm

An evolutionary performance-enhanced PSO approach by using a BP neural-learning-based PID controller

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