An improved particle swarm optimizer with difference mean based perturbation

Kundu, Rupam; Das, Swagatam; Mukherjee, Rajarshi; Debchoudhury, Shantanab

doi:10.1016/j.neucom.2013.09.026

Cited by 56 publications

(15 citation statements)

References 50 publications

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“…(10), the grey forecasting model of the primitive data sequences is finally obtained as [39]. As one of the intelligent optimization algorithms, the method was simple implementation [40,41]. It had better efficiency than other evolutionary computations to obtain the optimal solution.…”

Section: Methodology and Datamentioning

confidence: 99%

Forecasting of Wastewater Discharge and the Energy Consumption in China Based on Grey Model

Zou

2019

Mathematical Problems in Engineering

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Water pollution control is currently an important development strategy for China. Analysis and forecasting of wastewater discharge and the energy consumption are critical for this strategy. In this paper, we proposed the two time series models, namely, improved GM(0,n) model (IGM(0,n)) and optimized FGM(1,1) model (OFGM(1,1)). Two numerical experiments were used to test the validity of these models. It was indicated that the forecasting ability of two models had remarkably improved, compared to traditional FGM(1,1) and GM(0,n), respectively. We applied the two models to forecast wastewater discharge and energy consumption in China. The forecasting results have shown that the current growth rate in treated wastewater could meet the planning target of the 13th Five-Year Plan. The wastewater discharge and the energy consumption would reach 77.39 billion tons and 22.75 billion kWh, respectively, in 2020.

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Section: Methodology and Datamentioning

confidence: 99%

Forecasting of Wastewater Discharge and the Energy Consumption in China Based on Grey Model

Zou

2019

Mathematical Problems in Engineering

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“…From the different empirical researches, it has been proposed that the two acceleration constants should be c 1 = c 2 . r 1 and r 2 are uniformly distributed random numbers bound within the range [0, 1] and are instantiated freshly for each ordered pair [d, i] [28]. Particles velocity may be optionally clamped to a maximum denoted as…”

Section: Pso Basics and Optimization Mechanism Analysismentioning

confidence: 99%

Modified particle swarm optimization for BMDS interceptor resource planning

Fu-xian

Long

et al. 2015

Appl Intell

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One important mission of the strategic defense is to develop an integrated, layered Ballistic Missile Defense System (BMDS). We consider the problem of assigning interceptors to multiple waves of incoming ballistic missiles. This work addresses the issue of shot time constraints and directly allocates multiple targets to interceptors in the single engagement problem. A mathematical model and method for BMDS Interceptor resource planning (IRP) is presented. In addition to mathematical model, a modified particle swarm optimization RPPSO algorithm has been developed for solving complex IRP problem. This algorithm adapted a basic PSO algorithm to make it compatible with reasonable engineering problems. RPPSO was developed by embedding a reverse predictor within the basic algorithm to avoid premature convergence without significantly reducing convergence speed, and adding a repulsive force to keep the diversity of both local and global optima.

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“…More recently, Particle Swarm Optimization (PSO), another popular swarm intelligence optimization technique has been widely applied to optimization problems [9][10] [11]. Benefits including simple implementation, low computational cost and high efficiency, render PSO effectively in dealing with single objective optimization compared with EAs, and have led to adaptation for tackling MOO problems [12] [13] [14] [15].…”

Section: Introductionmentioning

confidence: 99%

A novel hybrid teaching learning based multi-objective particle swarm optimization

et al. 2017

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Article:Cheng, T., Chen, M., Fleming, P.J. orcid.org/0000-0001-9837-8404 et al. (2 more authors) (2017) A novel hybrid teaching learning based multi-objective particle swarm optimization. Neurocomputing, Abstract:How to obtain a good convergence and well-spread optimal Pareto front is still a major challenge for most meta-heuristic multi-objective optimization (MOO) methods. In this paper, a novel hybrid teaching learning based particle swarm optimization (HTL-PSO) with circular crowded sorting (CCS), named HTL-MOPSO, is proposed for solving MOO problems. Specifically, the new HTL-MOPSO combines the canonical PSO search with a teaching-learning-based optimization (TLBO) algorithm in order to improve search ability and speed up search procedure. Also, CCS technique is developed to improve the diversity and spread of solutions when truncating the external elitism archive. The performance of HTL-MOPSO algorithm was tested on several well-known benchmarks problems and compared with other state-of-the-art MOO algorithms in respect of convergence and spread of final solutions to the true Pareto front. Also, the individual contributions made by the strategies of HTL-PSO and CCS are analyzed. Experimental results validate the effectiveness of HTL-MOPSO and demonstrate its superior ability to find solutions of better spread and diversity, while assuring a good convergence.

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An improved particle swarm optimizer with difference mean based perturbation

Cited by 56 publications

References 50 publications

Forecasting of Wastewater Discharge and the Energy Consumption in China Based on Grey Model

Forecasting of Wastewater Discharge and the Energy Consumption in China Based on Grey Model

Modified particle swarm optimization for BMDS interceptor resource planning

A novel hybrid teaching learning based multi-objective particle swarm optimization

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