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Grey Wolf Optimizer (GWO) is a recently developed meta-heuristic search algorithm inspired by grey wolves (Canis lupus), which simulate the social stratum and hunting mechanism of grey wolves in nature and based on three main steps of hunting: searching for prey, encircling prey and attacking prey. This paper presents the application of GWO algorithm for the solution of non-convex and dynamic economic load dispatch problem (ELDP) of electric power system. The performance of GWO is tested for ELDP of small-, medium- and large-scale power systems, and the results are verified by a comparative study with lambda iteration method, Particle Swarm Optimization algorithm, Genetic Algorithm, Biogeography-Based Optimization, Differential Evolution algorithm, pattern search algorithm, NN-EPSO, FEP, CEP, IFEP and MFEP. Comparative results show that the GWO algorithm is able to provide very competitive results compared to other well-known conventional, heuristics and meta-heuristics search algorithms.

Uncertainties of renewable energy sources (RESs) such as wind turbine (WT) and photovoltaic (PV) units are one of the considerable challenges of prosumer microgrids (PMGs) for the optimal day‐ahead operation. In this study, a new probabilistic scenario‐based method of optimal scheduling and operation of PMGs is developed. In this regard, different scenarios are generated using Monte Carlo Simulations (MCS). Furthermore, k‐means, k‐medoids, and differential evolution algorithms (DEA) are deployed to cluster the scenarios in the proposed method. A realistic commercial PMG in Iran is selected to apply the introduced method. The validity of the developed probabilistic optimization method for PMG operation is examined by comparing the results under various scenario reduction algorithms and MCS ones. The comparison of the obtained results and those of other existing deterministic methods highlights the advantages of the presented method. Furthermore, the sensitivity analyses are carried out to investigate the robustness of the developed method against the increase in the system uncertainty level. According to the test results, it is concluded that the k‐medoids algorithm has the best performance in comparison with the k‐means and the DEA‐based clustering under various conditions. Proposing a novel scenario‐based O.F to optimize the operation costs of prosumers. Comparison of the proposed method and other available deterministic ones. Comparison of different scenario reduction methods. Validation of the scenario reduction‐based method by using MCS. Investigation of the proposed method robustness against the uncertainty increment.

Operating a reservoir during flooding is a complex problem in which optimum decision-making is a difficult task. The present study demonstrates a solution for the operation of flooding problem in a multiple-purpose reservoir. A reservoir on River Narmada in central India is chosen as the case study. The multiple objective problems comprised maximization of hydropower releases, minimizing spills, and achieving stipulated target storage at the end of the operation period. The chosen optimization models are the Differential Evaluation Algorithm (DEA) and its variants: the Enhanced Differential Evolution Algorithm (EDEA) and the Modified Enhanced Differential Evaluation Algorithm (MEDEA). The EDEA model is modified in the present study to MEDEA. The results of all three models applied to the same case study are compared on convergence to an optimal solution. All three algorithms were tested on two of the popular benchmark functions that are Ackley and Sphere. The results of both applications demonstrated that MEDEA proved to be the best in terms of converging to the optimal solution, exhibiting better stability, and quality of final results. The outcomes of this study also provided an effective way to optimize large scale multi-purpose and multi-reservoir flood control operation problems.

This paper presents a novel structural damage detection indicator, i.e., fourth-order voltage statistical moment (FVSM) based on the electromechanical impedance (EMI) principle, and then proposes a two-step damage detection method based on the novel indicator and a differential evolution algorithm (DEA). In this study, several lead zirconate titanate (PZT) sensors bonded to an experimental steel beam were utilized to acquire the time-domain voltage responses. On this basis, the fourth-order voltage statistical moments (FVSMs) of the voltage responses are computed to locate the damage element in the detected structure, and the proposed damage detection method is utilized to quantify the damage. In addition, theoretical PZT voltage responses are also calculated based on the piezoelectric theory and the spectral element method (SEM). Experimental results verify the accuracy of the theoretical voltage values and the effectiveness of the proposed damage indicator. Results indicate that the FVSM is effective in locating the damage element. Integrated with DEA, the proposed technique is capable of quantifying damage.

This work presents the newly formulated Dynastic Optimization Algorithm, DOA as applied to the wind turbine micrositing problem. The data is acquired by the use of the standard MATLAB software at a wind speed of 12 m/s. The values of the efficiency of the algorithm, cost per installation of per unit turbine, and total dissipated power at each number of turbines installed are discussed. This algorithm is applied to two test functions and the results are described therein. It has been welldemonstrated that the proposed DOA exhibits superior performance over GA and DEA for test functions by hitting the minima very often and with higher precision. On the other hand DOA performance on WTM problem is also encouraging.

In order to realize safe and accurate homing of parafoil system, a multiphase homing trajectory planning scheme is proposed according to the maneuverability and basic flight characteristics of the vehicle. In this scenario, on the basis of geometric relationship of each phase trajectory, the problem of trajectory planning is transformed to parameter optimizing, and then auxiliary population-based quantum differential evolution algorithm (AP-QDEA) is applied as a tool to optimize the objective function, and the design parameters of the whole homing trajectory are obtained. The proposed AP-QDEA combines the strengths of differential evolution algorithm (DEA) and quantum evolution algorithm (QEA), and the notion of auxiliary population is introduced into the proposed algorithm to improve the searching precision and speed. The simulation results show that the proposed AP-QDEA is proven its superior in both effectiveness and efficiency by solving a set of benchmark problems, and the multiphase homing scheme can fulfill the requirement of fixed-points and upwind landing in the process of homing which is simple in control and facile in practice as well.

Summary In the current paper, the social welfare maximization for alternating current–direct current (AC–DC) Deregulated Power System (DPS) based on evolutionary algorithms is introduced. The social welfare optimization based on evolutionary algorithms is very simple in comparison with numerical methods for complications of social welfare equations in the presence of high‐voltage direct current (HVDC) links, limitations of numerical methods in differentiable convex objective functions, and need of additional mathematical calculations and many variables especially for constraints. Furthermore, as a new merit of HVDC transmission lines besides all well‐known privileges, the advantage of the HVDC transmission line presence in a power system for social welfare maximization in comparison with a pure AC power system is shown. To reach this aim, the social welfare maximization is studied in two main steps. First, it is investigated in an AC DPS for four different scenarios. Then one of the most important AC lines transmitting a huge amount of power (operating at its thermal limit) is replaced with a HVDC link with exact the same transmission capacity; and the social welfare is maximized again for four aforementioned scenarios. The optimization process in all cases is performed using Differential Evolution Algorithm (DEA) that is selected among five different evolutionary methods, i.e., Particle Swarm Optimization, Shuffled Frog Leaping Algorithm, Teaching‐Learning‐Based, and Artificial Bee Colony besides DEA. The results show that the social welfare of the DPS in the presence of the HVDC link is enhanced remarkably during scenarios with high supply, in which congestion of AC transmission line is occurred. Copyright © 2014 John Wiley & Sons, Ltd.