MPSO: Modified particle swarm optimization and its applications

Tian, Decheng; Shi, Zhongzhi

doi:10.1016/j.swevo.2018.01.011

Cited by 291 publications

(146 citation statements)

References 57 publications

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“…In order to solve this problem, novel versions of the PSO algorithm have suggested. Such as orthogonal PSO algorithm (Al‐Bahrani & Patra, ) and Modified particle swarm optimization (Tian & Shi, ).…”

Section: Resultsmentioning

confidence: 99%

“…In order to solve this problem, novel versions of the PSO algorithm have suggested. Such as orthogonal PSO algorithm (Al-Bahrani & Patra, 2018) and Modified particle swarm optimization (Tian & Shi, 2018). to discriminate between individual bacteria colonies at both species and strain level is of great public health importance since most bacteria have many strains.…”

Section: Classificationmentioning

confidence: 99%

See 1 more Smart Citation

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Bonah

Huang

Ren

et al. 2019

J Food Process Engineering

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Rapid detection of bacterial foodborne pathogens is crucial in reducing the incidence of diseases associated with food contaminated with pathogens and toxins. This article presents a classification model of support vector machine (SVM) together with a metaheuristic framework using genetic algorithm (GA), particle swarm optimization (PSO), and grid searching (GS) optimization algorithms for bacterial foodborne pathogen classification and differentiation. LDA and SVM showed classification accuracies of training (90.3% and 91%) and prediction (89.5% and 90.6%), respectively using the Matlab classification learner app. Optimization of the modeling parameters c and g for SVM were performed to increase efficiency and classification accuracy. Simulated results show classification accuracies of 100% (training set) and 98.95% (prediction set) for five selected bacterial pathogens acquired using electronic nose dataset and PSO‐SVM model. Electronic nose recognition system made up of 12 metal oxide semiconductor sensors produced different distinctive response signals for each bacterial and could differentiate Escherichia coli, Escherichia coli O157: H7, Listeria monocytogenes, Salmonella enteritidis, and Salmonella Typhimurium. PSO‐SVM algorithm can be efficiently used in bacterial discrimination at the species and strain level by electronic nose and has the good ability both in learning and in generalization. Practical Applications Existing microbial methods depend on traditional culture‐based methods which are time‐wise lengthy, require trained and qualified personnel, and are not suitable as point‐of‐use (POU) sensing devices. The application of electronic nose for the detection and classification of bacterial foodborne pathogens have shown great promise and proven to be effective with increasing sensitivity and selectivity as compared to traditional methods. The ability for E‐nose to discriminate between individual bacteria colonies at both species and strain level is of great public health importance since most bacteria have many strains. Virulence and pathogenicity are often associated with only a subset of these strains and it is essential for a method to be able to differentiate between pathogenic and nonpathogenic strains during a foodborne outbreak.

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Section: Resultsmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Bonah

Huang

Ren

et al. 2019

J Food Process Engineering

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show abstract

“…However, the conventional CCGA has low search efficiency and may easily trap local optimum. The existing cooperative particle swarm optimization (CPSO) [35,36] can easily jump out of local optimum, but they lack effective cooperation between subpopulations and cannot be directly used for path-planning for multiple UAVs. To take full advantage of CPSO, a distributed cooperative particle swarm optimization (DCPSO) algorithm with cooperation is proposed.…”

Section: Dcpso With Cooperationmentioning

confidence: 99%

Path Planning for Multi-UAV Formation Rendezvous Based on Distributed Cooperative Particle Swarm Optimization

et al. 2019

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This paper studies the problem of generating cooperative feasible paths for formation rendezvous of unmanned aerial vehicles (UAVs). Cooperative path-planning for multi-UAV formation rendezvous is mostly a complicated multi-objective optimization problem with many coupled constraints. In order to satisfy the kinematic constraints, i.e., the maximum curvature constraint and the requirement of continuous curvature of the UAV path, the Pythagorean hodograph (PH) curve is adopted as the parameterized path because of its curvature continuity and rational intrinsic properties. Inspired by the co-evolutionary theory, a distributed cooperative particle swarm optimization (DCPSO) algorithm with an elite keeping strategy is proposed to generate a flyable and safe path for each UAV. This proposed algorithm can meet the kinematic constraints of UAVs and the cooperation requirements among UAVs. Meanwhile, the optimal or sub-optimal paths can be obtained. Finally, numerical simulations in 2-D and 3-D environments are conducted to demonstrate the feasibility and stability of the proposed algorithm. Simulation results show that the paths generated by the proposed DCPSO can not only meet the kinematic constraints of UAVs and safety requirements, but also achieve the simultaneous arrival and collision avoidance between UAVs for formation rendezvous. Compared with the cooperative co-evolutionary genetic algorithm (CCGA), the proposed DCPSO has better stability and a higher searching success rate.

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“…PSO is usually executed by repeatedly computing (1) and (2) until a specified number of iterations have reached or the velocity updates are close to zero during the iterations. The pseudo code for the PSO algorithm can be referred to the description proposed by Tian et al [4].…”

Section: Paritcle Swarm Optimizationmentioning

confidence: 99%

“…For its superiority, PSO has rapidly developed with applications in solving real-world optimization problems in recent years [2,3]. However, PSO is easily trapped into the local optima, and premature convergence appears when it is applied to complex multimodal problems [4]. Many attempts have been made to improve the performance of the PSO [5].…”

Section: Introductionmentioning

confidence: 99%

Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

Chen¹

2019

Information

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Motivated by concepts in quantum mechanics and particle swarm optimization (PSO), quantum-behaved particle swarm optimization (QPSO) was proposed as a variant of PSO with better global search ability. In this paper, a QPSO with weighted mean personal best position and adaptive local attractor (ALA-QPSO) is proposed to simultaneously enhance the search performance of QPSO and acquire good global optimal ability. In ALA-QPSO, the weighted mean personal best position is obtained by distinguishing the difference of the effect of the particles with different fitness, and the adaptive local attractor is calculated using the sum of squares of deviations of the particles’ fitness values as the coefficient of the linear combination of the particle best known position and the entire swarm’s best known position. The proposed ALA-QPSO algorithm is tested on twelve benchmark functions, and compared with the basic Artificial Bee Colony and the other four QPSO variants. Experimental results show that ALA-QPSO performs better than those compared method in all of the benchmark functions in terms of better global search capability and faster convergence rate.

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MPSO: Modified particle swarm optimization and its applications

Cited by 291 publications

References 57 publications

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Electronic nose classification and differentiation of bacterial foodborne pathogens based on support vector machine optimized with particle swarm optimization algorithm

Path Planning for Multi-UAV Formation Rendezvous Based on Distributed Cooperative Particle Swarm Optimization

Quantum-Behaved Particle Swarm Optimization with Weighted Mean Personal Best Position and Adaptive Local Attractor

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