Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators

Piotrowski, A.

doi:10.1016/j.ins.2013.03.060

Cited by 97 publications

(25 citation statements)

References 140 publications

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“…The cost and emission parameters of these DG sources are listed in Table . The capacity factor of the aforementioned sources used are gathered from the literature . The total power demand of the microgrid is 2.266 MW which is spread throughout the span of 24 hours as shown in Figure .…”

Section: Resultsmentioning

confidence: 99%

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Dynamic cost analysis of a grid connected microgrid using neighborhood based differential evolution technique

Dey

Bhattacharyya

2018

Int Trans Electr Energ Syst

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Summary Microgrids have lately proved to be the most efficient type of power system delivering uninterrupted power to a local area incurring negligible amount of transmission loss. Inclusion of the available renewable energy sources among the various distributed generation (DG) sources in a microgrid have decreased the emission of harmful toxic gases to a great extent. Dynamic economic dispatch indicates the economic dispatch of a microgrid allocating the timely optimal output of the distributed generation sources. However, the setup of a microgrid involves a huge amount of capital out of which installation cost, operation and maintenance cost, and depreciation cost instigated through the passing of time are a few. This paper uses three soft computing techniques viz. particle swarm optimization, differential evolution, and differential evolution with local global neighborhoods to perform a novel dynamic cost analysis of a microgrid and minimize its overall cost which includes fuel, emission, operation and maintenance cost, installation, and depreciation costs. Four different case studies are pulled off on a microgrid, and a comparative analysis is done to identify the best case. Numerical results and figures also point out the best optimization technique which was able to give the most efficient and minimal cost for the microgrid.

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Section: Resultsmentioning

confidence: 99%

“…The capacity factor of the aforementioned sources used are gathered from the literature. [33][34][35][36] The total power demand of the microgrid is 2.266 MW which is spread throughout the span of 24 hours as shown in Figure 2. The treatment cost of CO 2 is 0.21¥/kg, that of SO 2 is 14.842¥/kg, and NO x is 62.964¥/kg.…”

Section: Description Of the Systemmentioning

confidence: 99%

Dynamic cost analysis of a grid connected microgrid using neighborhood based differential evolution technique

Dey

Bhattacharyya

2018

Int Trans Electr Energ Syst

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“…In Omran et al (2009), by employing the concept of index neighborhoods, the bare bones DE algorithm was proposed. To balance the exploration and exploitation, an adaptive memetic DE was proposed (Piotrowski 2013), where the ring topology was used to construct the neighborhood of individuals for local model. In Hu et al (2014), the ring neighborhood topology was employed to improve a memetic DE algorithm.…”

Section: Related Workmentioning

confidence: 99%

Cellular direction information based differential evolution for numerical optimization: an empirical study

et al. 2015

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Differential evolution (DE) is a well-known evolutionary algorithm which has been successfully applied in many scientific and engineering fields. In most DE algorithms, the base and difference vectors for mutation are randomly selected from the current population. That is, the useful population information cannot be fully exploited to guide the search of DE through mutation. Furthermore, the selection of parents in mutation has been verified to be critical for the DE performance. Therefore, to alleviate this drawback and improve the performance of DE, a novel DE algorithm with a directional mutation based on cellular topology is proposed in this study. This proposed algorithm is named as Cellular Direction Information based DE (DE-CDI). In DE-CDI, the cellular topology is employed first to define a neighborhood for each individual of population and then the direction information based on the neighborhood is incorporated into the mutation operator. In this way, DE-CDI not only utilizes the neighborhood information to exploit the regions of better individuals and accelerate convergence, but also introduces the direction information to guide the search to the promising area. To evaluate the performance of the proposed method, DE-CDI is applied to the original DE algorithms, as well as several advanced DE variants. Experimental results demonstrate the high performance of DE-CDI for most DE algorithms studied.

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“…In this paper, we remove the refreshing gap m due to the self-adaptive selection scheme, resulting in almost the same execution probability of mCL. It has been demonstrated that, "for many problems of interest the random sampling may perform better than heuristic algorithms" (Piotrowski 2013). To obtain better results on such perhaps more common problems, we combine randomly regeneration with Henon map as a mutation operator.…”

Section: Why Do We Choose These Five Strategies?mentioning

confidence: 99%

Self-adapting hybrid strategy particle swarm optimization algorithm

et al. 2015

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Particle swarm optimization (PSO) algorithm has shown promising performances on various benchmark functions and engineering optimization problems. However, it is still difficult to achieve a satisfying trade-off between exploration and exploitation for all the optimization problems and different evolving stages. Furthermore, control parameters of some related mechanisms need pre-experience by the requirement of trial-and-error scheme. This paper presents a novel PSO algorithm, which adaptively adopts various search strategies, called Self-adapting Hybrid Strategy PSO (SaH-SPS). Unlike some other peer PSO variants, this method dynamically changes the probabilities of different strategies according to their previous successful searching memories, without any additional control parameters. The probabilities of different strategies would be re-initialized according to a proposed dynamic probabilistic model to diverse the population. Besides, particles are updated by probabilistically selected strategies after niching PSO with Ring topology. Moreover, a dynamic updating mechanism by niching PSO is proposed to guarantee the parallel searching capability during the whole evolution process. Thus, this proposed algorithm might be problem-independent and search-stageCommunicated by independent, yielding more satisfying solutions on various optimization problems. A comprehensive experimental study is conducted on 28 benchmark functions of CEC 2013 special session on real-parameter optimization, including shifted, rotated, multi-modal, high conditioned, expanded and composition problems, compared with several state-of-the-art variants of PSO and differential evolution (DE) algorithms. Comparison results show that SaHSPS obtains outstanding performances on the majority of the test problems. Moreover, a practical engineering problem, real power loss minimization of IEEE 30-bus power system, is used to further evaluate SaHSPS. The numerical results, compared with other stochastic search algorithms, show that SaHSPS could find high-quality solutions with higher probability.

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Adaptive Memetic Differential Evolution with Global and Local neighborhood-based mutation operators

Cited by 97 publications

References 140 publications

Dynamic cost analysis of a grid connected microgrid using neighborhood based differential evolution technique

Dynamic cost analysis of a grid connected microgrid using neighborhood based differential evolution technique

Cellular direction information based differential evolution for numerical optimization: an empirical study

Self-adapting hybrid strategy particle swarm optimization algorithm

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