An efficient oscillating inertia weight of particle swarm optimisation for tracking optima in dynamic environments

Kordestani, Javidan Kazemi; Rezvanian, Alireza; Meybodi, Mohammad Reza

doi:10.1080/0952813x.2015.1020521

Cited by 22 publications

(16 citation statements)

References 35 publications

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“…The inertia weight controls exploitation and exploration of the search space due to its capability of adjusting velocity (Mauro and Johann, 2011;Huynh and Dunnigan, 2012;Kordestani et al, 2016).…”

Section: Standard Pso Algorithm (Pso-s)mentioning

confidence: 99%

Impact of C-factor on PSO for solar PV-based BLDC motor drive control

Merugumalla

Navuri

2019

IJCISTUDIES

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The constriction factor (C-factor)-based particle swarm optimisation algorithm is proposed for the solar photovoltaic array powered brushless direct current (BLDC) motor drive control. The rotor position sensors are completely eliminated. Instead, it is determined by measuring the changes in back-emf. The CPSO algorithm is used for the tuning of proportional-integral-derivative (PID) controller parameters for the speed control of the drive. The BLDC motor drive is modelled in MATLAB/Simulink and trapezoidal back-emf waveforms are modelled as a function of rotor position using MATLAB code. This paper deals with rise time, settling time, peak overshoot and steady-state error under varying conditions and examines the effectiveness of the BLDC motor drive with the proposed algorithm. The comparison of simulation results of proposed algorithm with other PSO algorithms demonstrates the prominence of the constriction factor for the drive controller.

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Section: Standard Pso Algorithm (Pso-s)mentioning

confidence: 99%

Impact of C-factor on PSO for solar PV-based BLDC motor drive control

Merugumalla

Navuri

2019

IJCISTUDIES

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“…Wang et al [17] introduced a memory scheme to PSO that is triggered whenever the exploration of the population results in a peak to deal with dynamic environments. Recently, Kordestani et al [18] presented an oscillating triangular inertia weight in PSO, which is a time-varying inertia weight parameter, and investigated its performance on tracking optima in dynamic environments. The Ant Colony Optimization (ACO) algorithm has also been studied and adapted to work in dynamic environments.…”

Section: Introductionmentioning

confidence: 99%

“…Over the years, various dynamic optimization problems have been proposed to investigate the population-based metaheuristic approaches in the dynamic environments. Among these problems is the Moving peaks benchmark by Branke [31] that was employed in many works such as those studies done by Blackwell and Branke [15,16], Turky and Abdullah [27], and Kordestani et al [18] and the DF1 generator developed by Morrison and De Jong [32] that was used for testing metaheuristics in dynamic environments in works like that performed by Tfaili et al [19]. However, there was no unified method for constructing dynamic optimization problems across the real space, combinatorial space, and binary space.…”

Section: Introductionmentioning

confidence: 99%

“…Self-adaptive schemes maintain the diversity by adaptively improving the search behavior of the algorithm, thus reducing the need for the manual tuning of the algorithm parameters [24]. Examples of methods based on this scheme are available in [5,6,18]. The multipopulation strategies have been widely applied to maintain the diversity of the population in the dynamic optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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The Patch-Levy-Based Bees Algorithm Applied to Dynamic Optimization Problems

Hussein

Abdullah

Sahran

2017

Discrete Dynamics in Nature and Society

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Many real-world optimization problems are actually of dynamic nature. These problems change over time in terms of the objective function, decision variables, constraints, and so forth. Therefore, it is very important to study the performance of a metaheuristic algorithm in dynamic environments to assess the robustness of the algorithm to deal with real-word problems. In addition, it is important to adapt the existing metaheuristic algorithms to perform well in dynamic environments. This paper investigates a recently proposed version of Bees Algorithm, which is called Patch-Levy-based Bees Algorithm (PLBA), on solving dynamic problems, and adapts it to deal with such problems. The performance of the PLBA is compared with other BA versions and other state-of-the-art algorithms on a set of dynamic multimodal benchmark problems of different degrees of difficulties. The results of the experiments show that PLBA achieves better results than the other BA variants. The obtained results also indicate that PLBA significantly outperforms some of the other state-of-the-art algorithms and is competitive with others.

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“…Besides the AIWPSO, ABC and GABC algorithms introduced in Section 3, two new PSO methods that focus on the improvement of inertia weight and/or acceleration coefficients' parameters are adopted in order to make comparisons with our algorithm. One is the low-discrepancy sequence initialized PSO algorithm with high-order nonlinear time-varying inertia weight (LHNPSO) [36] and the other is the oscillating triangular inertia weight PSO (OTIWPSO) [37]. We apply these methods to the grasping posture control problem and compare the convergence performance and convergence time with the proposed AIWCPSO and AIWCPSO-S algorithms.…”

Section: Experimental Set-upmentioning

confidence: 99%

Grasping Posture Control Design for a Home Service Robot Using an ABC-Based Adaptive PSO Algorithm

Lin

Kuo

et al. 2016

International Journal of Advanced Robotic Systems

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In this paper, a grasping posture control for a robotic arm is developed based on novel adaptive particle swarm optimization (PSO) for the home service robot. To grasp an object using the robotic arm of the home-service robot, both the spatial coordinates of the target and the appropriate collocation of the grasping posture should be examined. In this paper, we present another method for dealing with this problem, which integrates the artificial bee colony (ABC) algorithm into the adaptive particle swarm optimization (APSO) algorithm, where the mutation concept of the scout bee in the ABC algorithm is used to increase the diversity of the particles. In addition, adaptive acceleration coefficients and adaptive inertia weight are presented to ameliorate the convergence rate of the PSO algorithm. We name this control scheme AIWCPSO-S, which represents Adaptive Inertia Weight and acceleration Coefficients PSO with the aid of the Scout bee. Performance comparisons of existing ABC, global ABC, adaptive inertia weight PSO, low-discrepancy sequence initialized PSO algorithm with high-order nonlinear time-varying inertia weight (LHNPSO), oscillating triangular inertia weight PSO (OTIWPSO) and AIWCPSO-S algorithms are conducted by computer simulations. The experiment results show that the presented algorithm gives the most correct and fastest convergence capability.

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An efficient oscillating inertia weight of particle swarm optimisation for tracking optima in dynamic environments

Cited by 22 publications

References 35 publications

Impact of C-factor on PSO for solar PV-based BLDC motor drive control

Impact of C-factor on PSO for solar PV-based BLDC motor drive control

The Patch-Levy-Based Bees Algorithm Applied to Dynamic Optimization Problems

Grasping Posture Control Design for a Home Service Robot Using an ABC-Based Adaptive PSO Algorithm

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