A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems

Ali, Ahmed Fouad; Tawhid, Mohamed A.

doi:10.1016/j.asej.2016.07.008

Cited by 103 publications

(47 citation statements)

References 11 publications

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“…While the merit of GA over PSO is the ability to control convergence, because GA operators such as mutation and crossover could effectively influence GA convergence. GA is so sensitive to the initial population, which whether the initial population is not selected well, the algorithm cannot coverage to the global optimum [25]. Garg compared the results of hybrid PSO-GA with PSO and GA [26].…”

Section: Optimization Algorithmsmentioning

confidence: 99%

Comparison of Optimal Hedging Policies for Hydropower Reservoir System Operation

Tayebiyan

Mohammad

Al‐Ansari

et al. 2019

Water

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Reservoir operation rules play an important role in regions economic development. Meanwhile, hedging policies are mostly applied for municipal, industrial, and irrigation water supplies from reservoirs and it is less used for reservoir operation for hydropower generation. The concept of hedging and rationing factors can be used to maintain the water in a reservoir for the sake of increasing water storage and water head for future use. However, water storage and head are the key factors in operation of reservoir systems for hydropower generation. This study investigates the applicability of seven competing hedging policies including four customary forms of hedging (1PHP, 2PHP, 3PHP, DHP) and three new forms of hedging rules (SOPHP, BSOPHP, SHPHP) for reservoir operation for hydropower generation. The models were constructed in MATLAB R2011b based on the characteristics of the Batang Padang hydropower reservoir system, Malaysia. In order to maximize the output of power generation in operational periods (2003–2009), three optimization algorithms namely particle swarm optimization (PSO), genetic algorithm (GA), and hybrid PSO-GA were linked to one of the constructed model (1PHP as a test) to find the most effective algorithm. Since the results demonstrated the superiority of the hybrid PSO-GA algorithm compared to either PSO or GA, the hybrid PSO-GA were linked to each constructed model in order to find the optimal decision variables of each model. The proposed methodology was validated using monthly data from 2010–2012. The results showed that there are no significant difference between the output of monthly mean power generation during 2003–2009 and 2010–2012.The results declared that by applying the proposed policies, the output of power generation could increase by 13% with respect to the historical management. Moreover, the discrepancies between mean power generations from highest to lowest months were reduced from 49 MW to 26 MW, which is almost half. This means that hedging policies could efficiently distribute the water-supply and power-supply in the operational period and increase the stability of the system. Among the studied hedging policies, SHPHP is the most convenient policy for hydropower reservoir operation and gave the best result.

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Section: Optimization Algorithmsmentioning

confidence: 99%

Comparison of Optimal Hedging Policies for Hydropower Reservoir System Operation

Tayebiyan

Mohammad

Al‐Ansari

et al. 2019

Water

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“…A range of new offspring is generated by applying crossover and mutation operator on the individuals which randomly modifies the individual contents. Any individuals with less performance are replaced systematically by the offspring's [12].The performances of these individuals are continuously evaluated until any stopping criterion is satisfied. We can summarize the genetic algorithm as follows.…”

Section: B Genetic Algorithmmentioning

confidence: 99%

“…Particle swarm optimization (PSO) is a popular optimization technique developed as an inspiration from the behaviour of swarm [12]. PSO search for a best optimal result by analyzing the activity and clustering of birds [13].Each individual in PSO is called as particles and the entire population is called a swarm.…”

Section: Particle Swarm Optimization Algorithmmentioning

confidence: 99%

Medical Image Compression using Neural Network with HGAPSO Optimization

H.K.*,

2019

IJITEE

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Lossy medical image compression has become increasingly attractive due to a drastic increase in the number of images used for diagnosis and treatment. The work focused on developing a feed-forward neural network for compression of medical images with optimization of weights using hybrid genetic and particle swarm (HGAPSO) optimization technique. The neural network can achieve a better compressed & decompressed image only with the proper training and optimized weights. Training algorithms such as back-propagation algorithm (BPA) traps to local minima rather than the global one which degrades the quality of a reconstructed image. In this work, HGAPSO optimization is adopted to overcome the drawback of BPA. HGAPSO parameters are carefully chosen to have better exploitation & exploration in the search area, which avoids the algorithm from trapping to the local minima. High-quality results of Genetic Algorithm (GA) obtained using selection, crossover and mutation can provide quality guidance for PSO which improves the results of the proposed system. The performance of the proposed work is evaluated on a raw medical image database based on PSNR, MSE, and CR. The experiment is simulated for 16-4-16 neural network architecture and a compression ratio of 75 % is achieved. The results obtained indicated that with proper training PSNR could be improved by 1.98 %.

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“…These vibrations lead to the damage of either a component or structural damage, or both. The flexible beam and the associated control system of the active vibration are utilized as the platform for case studies that may employ GRNN, BA, and/or GAs [6][7][8][9][10][11][12][13][14][15]. In this section, a cantilever beam is presented as shown in figure 2, which is considered with the following characteristics:…”

Section: Active Vibration Control (Avc)mentioning

confidence: 99%

“…These approaches often fail, especially in the instance of nonliner search space related to the parameters. Many bioinspired and artificial intelligent techniques have been addressed to overcome these limitations [6][7][8][9][10][11][12][13][14][15][16]. In this paper, Bull Genetic Algorithm (BGA) and the spiking neural networks have been utilized to identify the properties concerning the system identification and AVC for a flexible beam system.…”

Section: Introductionmentioning

confidence: 99%

Spiking Neural Network and Bull Genetic Algorithm for Active Vibration Control

Awadalla¹,

Computers²

2018

IJISA

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Abstract-Systems with flexible structures display vibration as a characteristic property. However, when exposed to disturbing forces, then the component and/or structural nature of such systems are damaged. Therefore, this paper proposes two heuristics approaches to reduce the unwanted structural response delivered due to the external excitation; namely, bull genetic algorithm and spiking neural network. The bull genetic algorithm is based on a new selection property inherited from the bull concept. On the other hand, spiking neural network possess more than one synaptic terminal between each neural network layer and each synaptic terminal is modelled with a different period of delay. Extensive simulations have been conducted using simulated platform of a flexible beam vibration. To validate the proposed approaches, we performed a qualitative comparison with other related approaches such as traditional genetic algorithm, general regression neural network, bees algorithm, and adaptive neuro-fuzzy inference system. Based on the obtained results, it is found that the proposed approaches have outperformed other approaches, while bull genetic algorithm has a 5.2% performance improvement over spiking neural network.

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A hybrid particle swarm optimization and genetic algorithm with population partitioning for large scale optimization problems

Cited by 103 publications

References 11 publications

Comparison of Optimal Hedging Policies for Hydropower Reservoir System Operation

Comparison of Optimal Hedging Policies for Hydropower Reservoir System Operation

Medical Image Compression using Neural Network with HGAPSO Optimization

Spiking Neural Network and Bull Genetic Algorithm for Active Vibration Control

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