Meta-heuristic Optimization Algorithms for Predicting the Scouring Depth Around Bridge Piers

An accurate estimation of bridge pier scour has been considered as one of the important parameters in designing of bridges. However, due to the numerous involved parameters and convolution of this phenomenon, many existing approaches cannot predict scour depth with an acceptable accuracy. Obtained results from the empirical relationships show that these relationships have low accuracy in determining the maximum scour depth and they need a high safety factor for many cases, which leads to uneconomic designs of bridges. To cover these disadvantages, three new models are provided to estimate the bridge pier scour using an adaptive network-based fuzzy inference system. The parameters of the system are optimized by using the colliding bodies optimization, enhanced colliding bodies optimization and vibrating particles system methods. To evaluate the efficiency of the proposed methods, their results were compared with those of simple adaptive network-based fuzzy inference system and its improved versions by using the particle swarm optimization and genetic algorithm as well as the empirical equations. Comparison of results showed that the new vibrating particles system based algorithm could find better results than other two ones. In addition, comparison of the results obtained by the proposed methods with those of the empirical relations confirmed the high performance of the new methods

SLIME MOULD ALGORITHM FOR PRACTICAL OPTIMAL POWER FLOW SOLUTIONS INCORPORATING STOCHASTIC WIND POWER AND STATIC VAR COMPENSATOR DEVICE

Purpose. This paper proposes the application procedure of a new metaheuristic technique in a practical electrical power system to solve optimal power flow problems, this technique namely the slime mould algorithm (SMA) which is inspired by the swarming behavior and morphology of slime mould in nature. This study aims to test and verify the effectiveness of the proposed algorithm to get good solutions for optimal power flow problems by incorporating stochastic wind power generation and static VAR compensators devices. In this context, different cases are considered in order to minimize the total generation cost, reduction of active power losses as well as improving voltage profile. Methodology. The objective function of our problem is considered to be the minimum the total costs of conventional power generation and stochastic wind power generation with satisfying the power system constraints. The stochastic wind power function considers the penalty cost due to the underestimation and the reserve cost due to the overestimation of available wind power. In this work, the function of Weibull probability density is used to model and characterize the distributions of wind speed. Practical value. The proposed algorithm was examined on the IEEE-30 bus system and a large Algerian electrical test system with 114 buses. In the cases with the objective is to minimize the conventional power generation, the achieved results in both of the testing power systems showed that the slime mould algorithm performs better than other existing optimization techniques. Additionally, the achieved results with incorporating the wind power and static VAR compensator devices illustrate the effectiveness and performances of the proposed algorithm compared to the ant lion optimizer algorithm in terms of convergence to the global optimal solution.Мета. У статті пропонується процедура застосування нового метаеврістіческого методу в реальній електроенергетичній системі для розв’язання задач оптимального потоку енергії, а саме алгоритму слизової цвілі, який заснований на поведінці рою і морфології слизової цвілі в природі. Дане дослідження спрямоване на тестування і перевірку ефективності запропонованого алгоритму для отримання хороших рішень для проблем оптимального потоку потужності шляхом включення пристроїв стохастичною вітрової генерації і статичних компенсаторів VAR. У зв'язку з цим, розглядаються різні випадки, щоб мінімізувати загальну вартість генерації, знизити втрати активної потужності і поліпшити профіль напруги. Методологія. В якості цільової функції завдання розглядається мінімальна сукупна вартість традиційної генерації електроенергії і стохастичної вітрової генерації при задоволенні обмежень енергосистеми. Стохастична функція енергії вітру враховує величини штрафів через недооцінку і резервні витрати через завищену оцінку доступної вітрової енергії. У даній роботі функція щільності ймовірності Вейбулла використовується для моделювання і характеристики розподілів швидкості вітру. Практична цінність. Запропонований алгоритм був перевірений на системі шин IEEE-30 і великий алжирської тестовій енергосистемі зі 114 шинами. У випадках, коли мета полягає в тому, щоб звести до мінімуму традиційне вироблення електроенергії, досягнуті результати в обох тестових енергосистемах показали, що алгоритм слизової цвілі функціонує краще, ніж інші існуючі методи оптимізації. Крім того, досягнуті результати з використанням вітрової енергії і статичного компенсатора VAR ілюструють ефективність і продуктивність запропонованого алгоритму в порівнянні з алгоритмом оптимізатора мурашиних левів з точки зору збіжності до глобального оптимального рішення

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

Two-Step Many-Objective Optimal Power Flow Based on Knee Point-Driven Evolutionary Algorithm

To coordinate the economy, security and environment protection in the power system operation, a two-step many-objective optimal power flow (MaOPF) solution method is proposed. In step 1, it is the first time that knee point-driven evolutionary algorithm (KnEA) is introduced to address the MaOPF problem, and thereby the Pareto-optimal solutions can be obtained. In step 2, an integrated decision analysis technique is utilized to provide decision makers with decision supports by combining fuzzy c-means (FCM) clustering and grey relational projection (GRP) method together. In this way, the best compromise solutions (BCSs) that represent decision makers' different, even conflicting, preferences can be automatically determined from the set of Pareto-optimal solutions. The primary contribution of the proposal is the innovative application of many-objective optimization together with decision analysis for addressing MaOPF problems. Through examining the two-step method via the IEEE 118-bus system and the real-world Hebei provincial power system, it is verified that our approach is suitable for addressing the MaOPF problem of power systems.Comment: Accepted by Journal Processe

An approach to solve OPF problems using a novel hybrid whale and sine cosine optimization algorithm

Nowadays, improvement in power system performance is essential to obtaine economic and technical benifits. To achieve this, optimize the large number of parameters in the system based on optimal power flow(OPF). For solving OPF problem efficiently, it needs robust and fast optimization techniques. This paper proposes the application of a newly developed hybrid Whale and Sine Cosine optimization algorithm to solve the OPF. It has been implemented for optimization of the control variables. The reduction of true power generation cost, emission, true power losses, and voltage deviation are considered as different objectives. The hybrid Whale and Sine Cosine optimization is validated by solving OPF problem with various intentions using IEEE30 bus system. To varidate the proposed technique, the results obtained from this are compared with other methods in the literature. The robustness achieved with the proposed algorithm has been analyzed for the considered OPF problem using statistical analysis and whisker plots

Analysis and optimization of material flow inside the system of rotary coolers and intake pipeline via discrete element method modelling

There is hardly any industry that does not use transport, storage, and processing of particulate solids in its production process. In the past, all device designs were based on empirical relationships or the designer's experience. In the field of particulate solids, however, the discrete element method (DEM) has been increasingly used in recent years. This study shows how this simulation tool can be used in practice. More specifically, in dealing with operating problems with a rotary cooler which ensures the transport and cooling of the hot fly ash generated by combustion in fluidized bed boilers. For the given operating conditions, an analysis of the current cooling design was carried out, consisting of a non-standard intake pipeline, which divides and supplies the material to two rotary coolers. The study revealed shortcomings in both the pipeline design and the cooler design. The material was unevenly dispensed between the two coolers, which combined with the limited transport capacity of the coolers, led to overflowing and congestion of the whole system. Therefore, after visualization of the material flow and export of the necessary data using DEM design measures to mitigate these unwanted phenomena were carried out.Web of Science117art. no. 184

Metaheuristics algorithms to identify nonlinear Hammerstein model: A decade survey

Metaheuristics have been acknowledged as an effective solution for many difficult issues related to optimization. The metaheuristics, especially swarm’s intelligence and evolutionary computing algorithms, have gained popularity within a short time over the past two decades. Various metaheuristics algorithms are being introduced on an annual basis and applications that are more new are gradually being discovered. This paper presents a survey for the years 2011-2021 on multiple metaheuristics algorithms, particularly swarm and evolutionary algorithms, to identify a nonlinear block-oriented model called the Hammerstein model, mainly because such model has garnered much interest amidst researchers to identify nonlinear systems. Besides introducing a complete survey on the various population-based algorithms to identify the Hammerstein model, this paper also investigated some empirically verified actual process plants results. As such, this article serves as a guideline on the fundamentals of identifying nonlinear block-oriented models for new practitioners, apart from presenting a comprehensive summary of cutting-edge trends within the context of this topic area