Notice of Retraction: A fuzzy-particle swarm optimization based algorithm for solving shortest path problem

Ghanizadeh, Afshin; Sinaie, Saman; Atapour-Abarghouei, Amir; Shamsuddin, Siti Mariyam

doi:10.1109/iccet.2010.5486167

Cited by 11 publications

(8 citation statements)

References 18 publications

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“…For instance in economic applications, profit and sales must be maximized and cost should be as low as possible [5]. The goal is to find the best solution x * from a set of possible solutions X according to a set of criteria F = {f 1 , f 2 , ..., f n }.…”

Section: Introductionmentioning

confidence: 99%

“…The goal is to find the best solution x * from a set of possible solutions X according to a set of criteria F = {f 1 , f 2 , ..., f n }. This set of criteria is expressed as a mathematical function named objective function [5].…”

Section: Introductionmentioning

confidence: 99%

“…The deterministic methods work based on heuristics, such as adding punishment to escape from local minima. Heuristics use the information currently gathered by the algorithm to help deciding which solution candidate should be tested next or how the next solution can be produced [5]. Probabilistic methods estimate probabilities to decide whether the search should depart from the neighbourhood of a local minimum.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The simplest methods rely on optimizing the weighted sum g(x) of all functions (criteria) f i (x) ∈ F [5]. The mathematical foundations for multiobjective optimization that considers conflicting criteria in a fair way was laid by Vilfredo Pareto 110 years ago [5]. Pareto optimization is based on the definition of domination: a solution x 1 dominates (is preferred to) solution x 2 ((x 1 x 2 )) if x 1 is better than x 2 in at least one objective function and not worse with respect to all other objectives.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Multiobjective Particle Swarm Optimization Using Fuzzy Logic

Yazdani

Kwaśnicka

Ortiz-Arroyo

2011

Computational Collective Intelligence. Technologies and Applications

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Abstract. The paper presents FMOPSO a multiobjective optimization method that uses a Particle Swarm Optimization algorithm enhanced with a Fuzzy Logic-based controller. Our implementation makes use of a number of fuzzy rules as well as dynamic membership functions to evaluate search spaces at each iteration. The method works based on Pareto dominance and was tested using standard benchmark data sets. Our results show that the proposed method is competitive with other approaches reported in the literature.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Multiobjective Particle Swarm Optimization Using Fuzzy Logic

Yazdani

Kwaśnicka

Ortiz-Arroyo

2011

Computational Collective Intelligence. Technologies and Applications

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show abstract

Hybrid particle swarm optimization incorporating fuzzy reasoning and weighted particle

Wang

Hsu

2015

Neurocomputing

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Fuzzy adaptive tuning of a particle swarm optimization algorithm for variable-strength combinatorial test suite generation

Zamli

Ahmed

Mahmoud

et al. 2018

Swarm Intelligence - Volume 3: Applications

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Combinatorial interaction testing is an important software testing technique that has seen lots of recent interest. It can reduce the number of test cases needed by considering interactions between combinations of input parameters. Empirical evidence shows that it effectively detects faults, in particular, for highly configurable software systems. In real-world software testing, the input variables may vary in how strongly they interact; variable strength combinatorial interaction testing (VS-CIT) can exploit this for higher effectiveness. The generation of variable strength test suites is a non-deterministic polynomial-time (NP) hard computational problem [1]. Research has shown that stochastic population-based algorithms such as particle swarm optimization (PSO) can be efficient compared to alternatives for VS-CIT problems. Nevertheless, they require detailed control for the exploitation and exploration trade-off to avoid premature convergence (i.e. being trapped in local optima) as well as to enhance the solution diversity. Here, we present a new variant of PSO based on Mamdani fuzzy inference system [2,3,4], to permit adaptive selection of its global and local search operations. We detail the design of this combined algorithm and evaluate it through experiments on multiple synthetic and benchmark problems.We conclude that fuzzy adaptive selection of global and local search operations is, at least, feasible as it performs only second-best to a discrete variant of PSO, called DPSO. Concerning obtaining the best mean test suite size, the fuzzy adaptation even outperforms DPSO occasionally. We discuss the reasons behind this performance and outline relevant areas of future work.

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Notice of Retraction: A fuzzy-particle swarm optimization based algorithm for solving shortest path problem

Cited by 11 publications

References 18 publications

Multiobjective Particle Swarm Optimization Using Fuzzy Logic

Multiobjective Particle Swarm Optimization Using Fuzzy Logic

Hybrid particle swarm optimization incorporating fuzzy reasoning and weighted particle

Fuzzy adaptive tuning of a particle swarm optimization algorithm for variable-strength combinatorial test suite generation

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