An Empirical Comparison of Particle Swarm and Predator Prey Optimisation

Silva, Arlindo; Neves, Ana; Costa, Ernesto

doi:10.1007/3-540-45750-x_13

Cited by 99 publications

(55 citation statements)

References 9 publications

(15 reference statements)

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“…Usually the value of λ is set to 0.729 with 12 4.1 cc ϕ =+= . 1 c and 2 c are usually both set to 2.05.…”

Section: Canonical Psomentioning

confidence: 99%

PSOPB: A Two-population Particle Swarm Optimizer Mimicking Facultative Bio-parasitic Behavior

Qin¹,

Li²,

Li³

et al. 2012

IJCIS

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Inspired by the phenomenon of bio-parasitic behavior in natural ecosystem, this paper presents a novel particle swarm optimizer named PSOPB, in which particles are composed of the host and the parasite population. In the presented algorithm, the two populations mimic facultative bio-parasitic behaviour and exchange particles according to particles' fitness values sorted of each population in a certain number of iterations. The parasite mutation and the host immunity are also considered to tie it closer to bio-parasitic behaviour as well as improve the algorithm performance. In order to embody the law of "survival of the fittest" in biological evolution, the particles with poor fitness value in the host population are removed and replaced by the same numbers of the re-initialization particles to maintain constant population size. The experimental results of a set of 10 benchmark functions demonstrate the presented algorithm's efficacy.

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“…Usually the value of λ is set to 0.729 with 12 4.1 cc ϕ =+= . 1 c and 2 c are usually both set to 2.05.…”

Section: Canonical Psomentioning

confidence: 99%

PSOPB: A Two-population Particle Swarm Optimizer Mimicking Facultative Bio-parasitic Behavior

Qin¹,

Li²,

Li³

et al. 2012

IJCIS

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“…In this initial study, we have not attempted parameter optimization for either algorithm. We note that a number of strategies have been suggested in the swarm literature to improve diversity (Silva et al, 2002), and it is likely that a significant improvement in GS performance can be obtained with the adoption of these A t-test and bootstrap (re-sampling) t-test were performed at the 95% confidence level on the best fitness values, and where one algorithm outperforms another on the best fitness is highlighted in bold.…”

Section: Mastermindmentioning

confidence: 99%

Grammatical Swarm: The generation of programs by social programming

O’Neill

Brabazon

2006

Nat Comput

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Abstract. This study examines Social Programming, that is, the construction of programs using a Social Swarm algorithm based on Particle Swarm Optimization. Each individual particle represents choices of program construction rules, where these rules are specified using a Backus-Naur Form grammar. This study represents the first instance of a Particle Swarm Algorithm being used to generate programs. A selection of benchmark problems from the field of Genetic Programming are tackled and performance is compared to Grammatical Evolution. The results demonstrate that it is possible to successfully generate programs using the Grammatical Swarm technique. An analysis of the Grammatical Swarm approach is presented on the dynamics of the search. It is found that restricting the search to the generation of complete programs, or with the use of a ratchet constraint forcing individuals to move only if a fitness improvement has been found, can have detrimental consequences for the swarms performance and dynamics.

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“…The SLs are updated by following the CL and using information acquired from their best positions. In this paper, predator-influenced civilized swarm optimization (PCSO) is proposed, based on the integration of CSO [5] and PPO [2]. In PCSO, the predator particle is always trying to chase the CL particle, which improves the search capability of the proposed technique.…”

Section: Introductionmentioning

confidence: 99%

Speech recognition using ANN and predator-influenced civilized swarm optimization algorithm

Mittal¹,

Sharma²

2016

Turk J Elec Eng & Comp Sci

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This paper proposes a hybrid optimization technique, predator-influenced civilized swarm optimization, by integrating civilized swarm optimization (CSO) and predator-prey optimization (PPO) techniques. CSO is the integration of the attributes of particle swarm optimization and a society civilization algorithm (SCA). In the SCA, the swarm is divided into a few societies, and each society has its own society leader (SL); other individuals of the society are termed society members. The combination of all such societies forms a civilization, and the best-performing SL becomes the civilization leader (CL). In CSO, SLs and members update their positions through the guidance of their own CL and SLs, respectively, along with their best positions. In the proposed technique, the PPO technique is integrated with CSO, in which a predator particle is included in the swarm. The predator always tries to chase the CL in a controlled manner, which maintains diversity in the population and avoids local optimum solutions. The proposed optimization technique is applied to optimize the weights and biases of an artificial neural network (ANN) trained for speech recognition. Two databases have been used; one is a TI-46 isolated word database in clean and noisy conditions, and the other is a self-recorded Hindi numeral database. To evaluate the performance of the proposed technique, 2 performance criteria, correlation coefficient and mean square error, are applied. The results obtained by an ANN with the proposed technique outperform the results obtained by an ANN trained with particle swarm optimization, PPO, CSO, and backpropagation techniques in terms of correlation coefficient and mean square error.

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An Empirical Comparison of Particle Swarm and Predator Prey Optimisation

Cited by 99 publications

References 9 publications

PSOPB: A Two-population Particle Swarm Optimizer Mimicking Facultative Bio-parasitic Behavior

PSOPB: A Two-population Particle Swarm Optimizer Mimicking Facultative Bio-parasitic Behavior

Grammatical Swarm: The generation of programs by social programming

Speech recognition using ANN and predator-influenced civilized swarm optimization algorithm

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