Discrete particle swarm optimization based on estimation of distribution for terminal assignment problems

Wang, Jiahai; Cai, Yiqiao; Zhou, Yue; Wang, Ronglong; Li, Caiwei

doi:10.1016/j.cie.2010.12.014

Cited by 16 publications

(16 citation statements)

References 29 publications

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“…Wang et al (2011) proposed a discrete particle PSO based on Estimation of Distribution Algorithm (EDA), named DPSO-EDA, for optimising TAP.…”

Section: Previous Workmentioning

confidence: 99%

“…We do not implement HNN-GA and DPSO-EDA since they use a method based on Hopfield neural network, which works with a binary representation. We took the results of HNN-GA and DPSO-EDA from (Wang et al, 2011) and we use 6 test instances to evaluate the quality of the proposed algorithms.…”

Section: Previous Workmentioning

confidence: 99%

“…The results of NHACO, DPSO and ABC are also compared with the results obtained with HNN-GA and DPSO-EDA. We took the results of HNN-GA and DPSO-EDA from Wang et al (2011).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Swarm optimisation algorithms applied to large balanced communication networks

Bernardino

Sánchez‐Pérez

et al. 2013

Journal of Network and Computer Applications

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“…Wang et al (2011) proposed a discrete particle PSO based on Estimation of Distribution Algorithm (EDA), named DPSO-EDA, for optimising TAP.…”

Section: Previous Workmentioning

confidence: 99%

Section: Previous Workmentioning

confidence: 99%

See 1 more Smart Citation

Swarm optimisation algorithms applied to large balanced communication networks

Bernardino

Sánchez‐Pérez

et al. 2013

Journal of Network and Computer Applications

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“…Modified PSO algorithm Initialize the swarm: Initialize velocity and position associated with each particle within predefined range, set algorithm parameters and input: c 1 , c 2 , m, k max , w max , w min , s start , s end , v min , v max , x min , x max Randomly initialize particle velocity and position 1 For all the 8 benchmark functions, the proposed RPPSO algorithm is compared with PSO, APSO [30] and DPSO [31] algorithm. General parameters set as following, (1) Population size: m =200; (2) function dimension: D =30; (3) acceleration constants: c 1 = c 2 =1.5; (4) inertia weight: w max and w min are set as 0.9 and 0.4; (4) maximum iterations: k max =500; (5) independent experiments: 30 times.…”

Section: Performance Evaluationmentioning

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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“…Rapid social and economic development and population growth, along with climate change, have aggravated water shortages [2][3][4][5][6][7][8]. The recent situation is unlike early water supply management.…”

Section: Introductionmentioning

confidence: 99%

An Ameliorative Whale Optimization Algorithm for Multi-Objective Optimal Allocation of Water Resources in Handan, China

Yan

Sha

Liu

et al. 2018

Water

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Abstract:With the deepening discrepancy between water supply and demand caused by water shortages, alleviating water shortages by optimizing water resource allocation has received extensive attention. How to allocate water resources optimally, rapidly, and effectively has become a challenging problem. Thus, this study employs a meta-heuristic swarm-based algorithm, the whale optimization algorithm (WOA). To overcome drawbacks like relatively low convergence precision and convergence rates, when applying the WOA algorithm to complex optimization problems, logistic mapping is used to initialize swarm location, and inertia weighting is employed to improve the algorithm. The resulting ameliorative whale optimization algorithm (AWOA) shows substantially enhanced convergence rates and precision than the WOA and particle swarm optimization algorithms, demonstrating relatively high reliability and applicability. A water resource allocation optimization model with optimal economic efficiency and least total water shortage volume is established for Handan, China, and solved by the AWOA. The allocation results better reflect actual water usage in Handan. In 2030, the p = 50% total water shortage is forecast as 404.34 × 10 6 m 3 or 14.8%. The shortage is mainly in the primary agricultural sector. The allocation results provide a reference for regional water resources management.

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Discrete particle swarm optimization based on estimation of distribution for terminal assignment problems

Cited by 16 publications

References 29 publications

Swarm optimisation algorithms applied to large balanced communication networks

Swarm optimisation algorithms applied to large balanced communication networks

Modified particle swarm optimization for BMDS interceptor resource planning

An Ameliorative Whale Optimization Algorithm for Multi-Objective Optimal Allocation of Water Resources in Handan, China

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