Particle swarm optimization using multi-level adaptation and purposeful detection operators

Xia, Xuewen; Xie, Chengwang; Wei, Bo; Hu, Zhongbo; Wang, Bojian; Jin, Chang

doi:10.1016/j.ins.2016.12.043

Cited by 31 publications

(12 citation statements)

References 27 publications

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“…In the past two decades, a number of variants to the original have been introduced attempting to further improve its performance. Typical variants can be summarized in the following four types: (1) neighborhood topology [29], [30]; (2) parameter control [31], [32]; (3) hybrid methods [33], [34] and (4) novel learning schemes [35], [36]. Since genetic algorithms (GAs) have good exploration ability, genetic learning PSO (GLPSO) has been proposed [37] to strength the performance of PSO by generating high-quality exemplars to guide the evolution of the particles [38].…”

Section: B Psomentioning

confidence: 99%

Particle Swarm Optimized Autonomous Learning Fuzzy System

Shen

Angelov

2021

IEEE Trans. Cybern.

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The antecedent and consequent parts of a first-order evolving intelligent system (EIS) determine the validity of the learning results and overall system performance. Nonetheless, the state-of-the-art techniques mostly stress on the novelty from the system identification point of view but pay less attention to the optimality of the learned parameters. Using the recently introduced autonomous learning multiple model (ALMMo) system as the implementation basis, this paper introduces a particle swarmbased approach for EIS optimization. The proposed approach is able to simultaneously optimize the antecedent and consequent parameters of ALMMo and effectively enhance the system performance by iteratively searching for optimal solutions in the problem spaces. In addition, the proposed optimization approach does not adversely influence the "one pass" learning ability of ALMMo. Once the optimization process is complete, ALMMo can continue to learn from new data to incorporate unseen data patterns recursively without a full retraining. Experimental studies with a number of real-world benchmark problems validate the proposed concept and general principles. It is also verified that the proposed optimization approach can be applied to other types of EISs with similar operating mechanisms.

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Section: B Psomentioning

confidence: 99%

Particle Swarm Optimized Autonomous Learning Fuzzy System

Shen

Angelov

2021

IEEE Trans. Cybern.

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“…The experimental outcomes produced on URLPs i.e. URLP1-URLP4 by ePSO are given in Table 2-5 respectively and it is also compared with 13 others classical optimization algorithm namely DMSPSO [41], F-PSO [42], OLPSO [43], SLPSO [44], PSODDS [45], SL-PSO [46], HCLPSO [47], SSS-APSO [48], SopPSO [49], JADE [50], SaDE [51], CoDE [52] and CMA-ES [53]. The Tables 2 to 5 comprise best and mean values more than 30 independent runs with ranking i.e.…”

Section: B On Four Unconstrained Real Life Problemsmentioning

confidence: 99%

Enriched Particle Swarm Optimization Created By Varying Parameters for Optimization

Verma*¹,

Parouha²

2019

IJRTE

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Particle swarm optimization (PSO) is one of the most capable algorithms that reside to the swarm intelligence (SI) systems. Recently, it becomes very popular and renowned because of the easy implementation in complex/real life optimization problems. However, PSO has some observable drawbacks such as diversity maintenance, pre convergence and/or slow convergence speed. The ultimate success of PSO depends on the velocity update of the particles. Velocity has a significant dependence on its multiplied coefficient like inertia weight and acceleration factors. To increase the ability of PSO, this paper introduced an enriched PSO (namely ePSO), to solve hard optimization problems more precisely, efficiently and reliably. In ePSO novel gradually decreased inertia weight (as an alternative of a fixed constant value) and new gradually decreased and/or increased acceleration factors (meant for cognitive and social modules) is introduced. Proposed ePSO is used to solve four well known typical unconstrained benchmark functions and four complex unconstrained real life problems. The overall observation shows that proposed new algorithm ePSO is fitter than the compared algorithms significantly and statistically. Moreover, the convergence accuracy and speed of ePSO are also improved effectively

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“…Ghasemi et al [26] proposed Gaussian barebones TLBO (GBTLBO) by using Gaussian sampling technology, and the GBTLBO was applied to the optimal reactive power dispatch problem. (3) Initialize learners and evaluate them; (4) while stopping condition is not met (5) Choose the best learner as x teacher ; (6) Calculate the mean x mean of all learners; (7) for each learner x (8) // Teacher phase // (9) T F = round(1 + rand(0, 1)); (10) Update the learner according to Eq. (1); (11) Evaluate the new learner x ,new ; (12) Accept x ,new if it is better than the old one x ,old (13) // Learner phase // (14) Randomly select another learner x which is different from x ; (15) Update the learner according to Eq.…”

Section: Improvements On Tlbomentioning

confidence: 99%

“…Generate a random in [0, 1]; (8) if ≤ LE 9Update learner x according to Eq. (5); (10) Evaluate the new learner x ,new ; (11) Accept the new learner x ,new if it is better than the old one x ,old ; (12) end if (13) end for (14) end Algorithm 2: Learning enthusiasm based teacher phase.…”

Section: Learning Enthusiasm Based Learner Phasementioning

confidence: 99%

“…Most of these MS algorithms are natureinspired, and several of the prominent algorithms include genetic algorithms (GA) [1], evolution strategies (ES) [2], differential evolution (DE) [3], particle swarm optimization (PSO) [4,5], harmony search (HS) [6], and biogeographybased optimization (BBO) [7,8]. However, the "No Free Lunch" theorem suggests that no single algorithm is suitable for all problems [9]; therefore, more research is required to develop novel algorithms for different optimization problems with high efficiency [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

et al. 2018

Journal of Applied Mathematics

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Teaching-learning-based optimization (TLBO) is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO). In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others; by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems.

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Particle swarm optimization using multi-level adaptation and purposeful detection operators

Cited by 31 publications

References 27 publications

Particle Swarm Optimized Autonomous Learning Fuzzy System

Particle Swarm Optimized Autonomous Learning Fuzzy System

Enriched Particle Swarm Optimization Created By Varying Parameters for Optimization

Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

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