Reduction of Real Power Loss and Safeguarding of Voltage Constancy by Artificial Immune System Algorithm

In this paper, Artificial Immune System (AIS) algorithm is used for solving reactive power problem. Artificial Immune System Algorithm, also termed as the machine learning approach to Artificial Intelligence, are powerful stochastic optimization techniques with potential features of random search, hill climbing, statistical sampling and competition. Artificial immune system algorithmic approach to power system optimization these ideas are embedded into proposed algorithm for solving reactive dispatch problem. In order to evaluate the proposed algorithm, it has been tested in standard IEEE 30,118 bus systems and compared to other specified algorithms. Simulation results show better performance of the proposed AIS algorithm in reducing the real power loss and preservation of voltage stability

The Deployment in the Wireless Sensor Networks: Methodologies, Recent Works and Applications

International audienceThe wireless sensor networks (WSN) is a research area in continuous evolution with a variety of application contexts. Wireless sensor networks pose many optimization problems, particularly because sensors have limited capacity in terms of energy, processing and memory. The deployment of sensor nodes is a critical phase that significantly affects the functioning and performance of the network. Often, the sensors constituting the network cannot be accurately positioned, and are scattered erratically. To compensate the randomness character of their placement, a large number of sensors is typically deployed, which also helps to increase the fault tolerance of the network. In this paper, we are interested in studying the positioning and placement of sensor nodes in a WSN. First, we introduce the problem of deployment and then we present the latest research works about the different proposed methods to solve this problem. Finally, we mention some similar issues related to the deployment and some of its interesting applications

Новые подходы к управлению ценами на транспортные услуги

Development of new approaches to formation of analytics mechanisms for the purpose of pricing management of services is an important aspect of increasing the efficiency of transport management processes.Research aimed at improving the tools for determining the optimal parameters of the ratio of quality and price of service for formation of a competitive and efficient tariff policy continues to remain relevant and in demand in modern market conditions. The objective of the study, presented in the article, is to analyse and evaluate the prospects for implementation of the areas to improve the apparatus for assessing the price elasticity of demand for railway passenger transport services as the transition to the use of non-linear parameters in terms of customer behaviour modelling functions, as well as introduction of the most effective algorithms from the set of modern global mathematical optimisation tools.The research conclusions are based on the use of system analysis mechanisms, methods of economic and mathematical modelling and optimisation, as well as of non-parametric statistics tools.The results based on the use of an array of data on the demand of passengers of branded trains include: a comparative assessment of quality of modelling the price elasticity of demand using 15 functions that are nonlinear in terms of parameters; the most promising tools of the search for unknown parameters for non-smooth nonlinear functions for modelling the behaviour of railway customers are identified based on a three-stage procedure for comparative analysis of the performance of more than 60 optimisation algorithms (including the calculation of minima and medians for the sums of squares of modelling errors, bootstrap analysis, Kruskal– Wallace and Mann–Whitney tests, as well as the calculation of a metric specially developed by the authors for assessing the degree of superiority of one algorithm over another within the framework of non-parametric analysis).The findings seem able to be successfully used in relation to other modes of transport in solving similar problems of developing an effective toolkit for managing the prices of transport services.Важным аспектом повышения эффективности процессов управления на транспорте является развитие новых подходов к формированию механизмов аналитики для целей управления ценами услуг.В современных рыночных условиях продолжают оставаться актуальными и востребованными исследования, направленные на совершенствование инструментария определения оптимальных параметров соотношения качества и стоимости обслуживания для формирования конкурентоспособной и эффективной тарифной политики.Цель исследования, представленного в статье, – анализ и оценка перспектив реализации таких направлений по совершенствованию аппарата оценки ценовой эластичности спроса на услуги железнодорожного пассажирского транспорта, как переход к использованию нелинейных по параметрам функций моделирования поведения клиентов, а также внедрение наиболее эффективных алгоритмов из арсенала современного инструментария глобальной математической оптимизации.Формирование выводов исследования основывается на применении механизмов системного анализа, методов экономико-математического моделирования и оптимизации, а также инструментария непараметрической статистики.В итоге, на основе использования массива данных о спросе пассажиров фирменных поездов проведена сравнительная оценка качества моделирования ценовой эластичности спроса при использовании 15 нелинейных по параметрам функций, а также, в результате осуществления трёхэтапной процедуры сравнительного анализа эффективности работы более чем 60 алгоритмов оптимизации (включающей, в том числе, расчёт минимумов и медиан для сумм квадратов ошибок моделирования, бутстреп-анализ, тесты Краскела–Уоллеса и Манна–Уитни, а также расчёт специально разработанной авторами метрики оценки степени превосходства одного алгоритма над другим в рамках непараметрического анализа) определены наиболее перспективные механизмы поиска неизвестных параметров для негладких нелинейных функций моделирования поведения клиентов железнодорожного транспорта.Представляется, что полученные выводы могут быть успешно использованы и применительно к другим видам транспорта при решении ими аналогичных задач формирования эффективного инструментария управления ценами транспортных услуг

Modified moth swarm algorithm for optimal economic load dispatch problem

In this study, optimal economic load dispatch problem (OELD) is resolved by a novel improved algorithm. The proposed modified moth swarm algorithm (MMSA), is developed by proposing two modifications on the classical moth swarm algorithm (MSA). The first modification applies an effective formula to replace an ineffective formula of the mutation technique. The second modification is to cancel the crossover technique. For proving the efficient improvements of the proposed method, different systems with discontinuous objective functions as well as complicated constraints are used. Experiment results on the investigated cases show that the proposed method can get less cost and achieve stable search ability than MSA. As compared to other previous methods, MMSA can archive equal or better results. From this view, it can give a conclusion that MMSA method can be valued as a useful method for OELD problem

免疫学的および進化的アルゴリズムに基づく改良された群知能最適化に関する研究

富山大学・富理工博甲第175号・楊玉・2020/3/24富山大学202

Optimal Allocation of Distributed Generation with the Presence of Photovoltaic and Battery Energy Storage System Using Improved Barnacles Mating Optimizer

This paper proposes an improved version of Barnacles mating optimizer (BMO) for solving the optimal allocation problem of distribution generator (DGs) in radial distribution systems (RDSs). BMO is a recent bioinspired optimization algorithm that mimics the intelligence behavior of Barnacles\u27 mating. However, like with any metaheuristic optimization approach, it may face issues such as local optima trapping and low convergence rate. Hence, an improved BMO is adopted based on the quasi oppositional (QOBMO) and the chaos maps theories (CQOBMO). The two improvement methods are applied to increase the convergence performance of the conventional BMO. To prove the efficiency of the improved QOBMO and CQOBMO algorithms, 23 benchmark functions are used, and the accomplished results are compared with the conventional BMO. Then, the improved algorithm is applied to minimize the total power and energy losses in the distribution systems considering the uncertainty of DG power generation and time‐varying load demand. The uncertainty of DG is represented using photovoltaic‐based DG (PVDG). The improved method is employed to find the optimal power scheduling of PVDG and battery energy storage (BES) during 24 h. Two standard IEEE RDS (IEEE 33‐bus and IEEE 69‐bus) are used to simulate the case studies. Finally, the obtained results show that significant loss reductions (LRs) are achieved using the improved BMO where LRs reach 65.26%, and 68.86% in IEEE 33‐bus and 69‐bus, respectively, in the case of PVDG integration. However, using PVDG and BES the energy loss reductions reach 64% and 67.80% in IEEE 33‐bus and 69‐bus, respectively, which prove the efficiency of the improved BMO algorithm in finding the optimal solutions obtained so far

Pattern Nulling of Linear Antenna Arrays Using Backtracking Search Optimization Algorithm

An evolutionary method based on backtracking search optimization algorithm (BSA) is proposed for linear antenna array pattern synthesis with prescribed nulls at interference directions. Pattern nulling is obtained by controlling only the amplitude, position, and phase of the antenna array elements. BSA is an innovative metaheuristic technique based on an iterative process. Various numerical examples of linear array patterns with the prescribed single, multiple, and wide nulls are given to illustrate the performance and flexibility of BSA. The results obtained by BSA are compared with the results of the following seventeen algorithms: particle swarm optimization (PSO), genetic algorithm (GA), modified touring ant colony algorithm (MTACO), quadratic programming method (QPM), bacterial foraging algorithm (BFA), bees algorithm (BA), clonal selection algorithm (CLONALG), plant growth simulation algorithm (PGSA), tabu search algorithm (TSA), memetic algorithm (MA), nondominated sorting GA-2 (NSGA-2), multiobjective differential evolution (MODE), decomposition with differential evolution (MOEA/D-DE), comprehensive learning PSO (CLPSO), harmony search algorithm (HSA), seeker optimization algorithm (SOA), and mean variance mapping optimization (MVMO). The simulation results show that the linear antenna array synthesis using BSA provides low side-lobe levels and deep null levels

Symbiotic Organisms Search Algorithm: theory, recent advances and applications

The symbiotic organisms search algorithm is a very promising recent metaheuristic algorithm. It has received a plethora of attention from all areas of numerical optimization research, as well as engineering design practices. it has since undergone several modifications, either in the form of hybridization or as some other improved variants of the original algorithm. However, despite all the remarkable achievements and rapidly expanding body of literature regarding the symbiotic organisms search algorithm within its short appearance in the field of swarm intelligence optimization techniques, there has been no collective and comprehensive study on the success of the various implementations of this algorithm. As a way forward, this paper provides an overview of the research conducted on symbiotic organisms search algorithms from inception to the time of writing, in the form of details of various application scenarios with variants and hybrid implementations, and suggestions for future research directions

A comprehensive survey on cultural algorithms

Peer reviewedPostprin

自然に学ぶ知的アルゴリズムによる最適化及び予測問題に関する研究

富山大学・富理工博甲第147号・劉燕婷・2018/09/28富山大学201