scholarly journals Dynamic Inertia Weight Particle Swarm Optimization for Solving Nonogram Puzzles

2016 ◽  
Vol 7 (10) ◽  
Author(s):  
Habes Alkhraisat ◽  
Hasan Rashaideh
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Dynamic Inertia Weight ◽  
Dynamic Inertia

scholarly journals An Improved Opposition-Based Learning Particle Swarm Optimization for the Detection of SNP-SNP Interactions

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Junliang Shang ◽  
Yan Sun ◽  
Shengjun Li ◽  
Jin-Xing Liu ◽  
Chun-Hou Zheng ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Search Space ◽  
Age Related Macular Degeneration ◽  
Data Set ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Opposition Based Learning ◽  
Dynamic Inertia Weight ◽  
Dynamic Inertia

SNP-SNP interactions have been receiving increasing attention in understanding the mechanism underlying susceptibility to complex diseases. Though many works have been done for the detection of SNP-SNP interactions, the algorithmic development is still ongoing. In this study, an improved opposition-based learning particle swarm optimization (IOBLPSO) is proposed for the detection of SNP-SNP interactions. Highlights of IOBLPSO are the introduction of three strategies, namely, opposition-based learning, dynamic inertia weight, and a postprocedure. Opposition-based learning not only enhances the global explorative ability, but also avoids premature convergence. Dynamic inertia weight allows particles to cover a wider search space when the considered SNP is likely to be a random one and converges on promising regions of the search space while capturing a highly suspected SNP. The postprocedure is used to carry out a deep search in highly suspected SNP sets. Experiments of IOBLPSO are performed on both simulation data sets and a real data set of age-related macular degeneration, results of which demonstrate that IOBLPSO is promising in detecting SNP-SNP interactions. IOBLPSO might be an alternative to existing methods for detecting SNP-SNP interactions.

scholarly journals Optimal PV Parameter Estimation via Double Exponential Function-Based Dynamic Inertia Weight Particle Swarm Optimization

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4037 ◽  
Author(s):  
Arooj Tariq Kiani ◽  
Muhammad Faisal Nadeem ◽  
Ali Ahmed ◽  
Irfan Khan ◽  
Rajvikram Madurai Elavarasan ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Particle Swarm Optimization ◽  
Pv System ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Pv Module ◽  
Double Exponential ◽  
Dynamic Inertia Weight ◽  
Dynamic Inertia ◽  
Double Exponential Function

Parameters associated with electrical equivalent models of the photovoltaic (PV) system play a significant role in the performance enhancement of the PV system. However, the accurate estimation of these parameters signifies a challenging task due to the higher computational complexities and non-linear characteristics of the PV modules/panels. Hence, an effective, dynamic, and efficient optimization technique is required to estimate the parameters associated with PV models. This paper proposes a double exponential function-based dynamic inertia weight (DEDIW) strategy for the optimal parameter estimation of the PV cell and module that maintains an appropriate balance between the exploitation and exploration phases to mitigate the premature convergence problem of conventional particle swarm optimization (PSO). The proposed approach (DEDIWPSO) is validated for three test systems; (1) RTC France solar cell, (2) Photo-watt (PWP 201) PV module, and (3) a practical test system (JKM330P-72, 310 W polycrystalline PV module) which involve data collected under real environmental conditions for both single- and double-diode models. Results illustrate that the parameters obtained from proposed technique are better than those from the conventional PSO and various other techniques presented in the literature. Additionally, a comparison of the statistical results reveals that the proposed methodology is highly accurate, reliable, and efficient.

scholarly journals Array Pattern Synthesis Using Particle Swarm Optimization with Dynamic Inertia Weight

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Chuang Han ◽  
Ling Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Fitness Function ◽  
Particle Swarm ◽  
Main Beam ◽  
Pattern Synthesis ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
Initial Space ◽  
Array Pattern ◽  
Dynamic Inertia Weight

A Feedback Particle Swarm Optimization (FPSO) with a family of fitness functions is proposed to minimize sidelobe level (SLL) and control null. In order to search in a large initial space and converge fast in local space to a refined solution, a FPSO with nonlinear inertia weight algorithm is developed, which is determined by a subtriplicate function with feedback taken from the fitness of the best previous position. The optimized objectives in the fitness function can obtain an accurate null level independently. The directly constrained SLL range reveals the capability to reduce SLL. Considering both element positions and complex weight coefficients, a low-level SLL, accurate null at specific directions, and constrained main beam are achieved. Numerical examples using a uniform linear array of isotropic elements are simulated, which demonstrate the effectiveness of the proposed array pattern synthesis approach.

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