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Abstract: This paper presents combination of differential evolution (DE) and biogeography-based optimization (BBO) algorithm to solve complex economic emission load dispatch (EELD) problems of thermal generators of power systems. Emission substances like NO X , SO X , COX, Power demand equality constraint and operating limit constraint are considered here. Differential evolution (DE) is one of the very fast and robust, accurate evolutionary algorithms for global optimization and solution of EELD problems. Biogeography-based optimization (BBO) is another new biogeography inspired algorithm. Biogeography deals with the geographical distribution of different biological species. This algorithm searches for the global optimum mainly through two steps: migration and mutation. In this paper combination of DE and BBO (DE/BBO) is proposed to accelerate the convergence speed of both the algorithm and to improve solution quality. To show the advantages of the proposed algorithm, it has been applied for solving multi-objective EELD problems in a 3-generator system with NO X and SO X emission, in a 6-generators system considering NO X emission, in a 6-generator system addressing both valve-point loading and NO X emission. The current proposal is found better in terms of quality of the compromising and individual solution obtained. [Copyright &y& Elsevier]

This paper presents a new hybrid global optimization method referred to as DESA. The algorithm exploits random sampling and the metropolis criterion from simulated annealing to perform global search. The population of points and efficient search strategy of differential evolution are used to speed up the convergence. The algorithm is easy to implement and has only a few parameters. The theoretical global convergence is established for the hybrid method. Numerical experiments on 23 mathematical test functions have shown promising results. The method was also integrated into SPICE OPUS circuit simulator to evaluate its practical applicability in the area of analog integrated circuit sizing. Comparison was made with basic simulated annealing, differential evolution, and a multistart version of the constrained simplex method. The latter was already a part of SPICE OPUS and produced good results in past research. [ABSTRACT FROM AUTHOR]

An improved differential evolution (DE) method based on the dynamic search strategy (IDEBDSS) is proposed to solve dynamic economic dispatch problem with valve-point effects in this paper. The proposed method combines the DE algorithm with the dynamic search strategy, which improves the performance of the algorithm. DE is the main optimizer in the method proposed. While chaotic sequences are applied to obtain the dynamic parameter settings in DE, dynamic search strategy which consists of two steps, global search strategy and local search strategy, is used to improve algorithm efficiency. To accelerate convergence, a new infeasible solution handing method is adopted in the local search strategy; meanwhile, an orthogonal crossover (OX) operator is added to the global search strategy to enhance the optimization search ability. Finally, the feasibility and effectiveness of the proposed methods are demonstrated by three test systems, and the simulation results reveal that the IDEBDSS method can obtain better solutions with higher efficiency than the standard DE and other methods reported in the recent literature. [ABSTRACT FROM AUTHOR]

Many improved differential Evolution (DE) algorithms have emerged as a very competitive class of evolutionary computation more than a decade ago. However, few improved DE algorithms guarantee global convergence in theory. This paper developed a convergent DE algorithmin theory, which employs a self-adaptation scheme for the parameters and two operators, that is, uniform mutation and hidden adaptation selection (haS) operators. The parameter self-adaptation and uniformmutation operator enhance the diversity of populations and guarantee ergodicity. The haS can automatically remove some inferior individuals in the process of the enhancing population diversity. The haS controls the proposed algorithm to break the loop of current generation with a small probability. The breaking probability is a hidden adaptation and proportional to the changes of the number of inferior individuals. The proposed algorithm is tested on ten engineering optimization problems taken from IEEE CEC2011. [ABSTRACT FROM AUTHOR]

This paper presents a comparison between new approaches introducing different chaotic maps with ergodicity, irregularity, and the stochastic property in Differential Evolution algorithm (DE). The members of the new family so-called Chaotic Differential Evolution (CDE) algorithms employ chaos in order to improve the global convergence by escaping the local solutions. [ABSTRACT FROM AUTHOR]

Abstract: Differential evolution (DE) is a simple and efficient global optimization algorithm. However, DE has been shown to have certain weaknesses, especially if the global optimum should be located using a limited number of function evaluations (NFEs). Hence hybridization with other methods is a research direction for the improvement of differential evolution. In this paper, a hybrid DE based on the one-step k-means clustering and 2 multi-parent crossovers, called clustering-based differential evolution with 2 multi-parent crossovers (2-MPCs-CDE) is proposed for the unconstrained global optimization problems. In 2-MPCs-CDE, k cluster centers and several new individuals generate two search spaces. These spaces are then searched in turn. This method utilizes the information of the population effectively and improves search efficiency. Hence it can enhance the performance of DE. A comprehensive set of 35 benchmark functions is employed for experimental verification. Experimental results indicate that 2-MPCs-CDE is effective and efficient. Compared with other state-of-the-art evolutionary algorithms, 2-MPCs-CDE performs better, or at least comparably, in terms of the solution accuracy and the convergence rate. [Copyright &y& Elsevier]

Abstract: In many chemical engineering applications, one frequently encounters dynamic optimization problems. The solution of these types of problems is usually very difficult due to their highly nonlinear, multidimensional and multimodal nature. In fact, several deterministic techniques have been proposed to solve these problems but difficulties related to ease of implementation, global convergence, and good computational efficiency have been frequently found. Recently, evolutionary algorithms (EAs) are gaining popularity for solving complex problems encountered in many engineering disciplines. They are found to be robust and more likely to locate global optimum as compared to several deterministic (gradient based) optimization methods. This paper deals with the application and evaluation of one such algorithm called trigonometric differential evolution (TDE) algorithm for solving dynamic optimization problems encountered in chemical engineering. This is a modified version of differential evolution (DE) which provides enhanced convergence speed. Both DE and TDE algorithms have been applied to seven dynamic optimization problems (five optimal control problems and two kinetic parameter estimation problems) taken from recent literature. The obtained numerical simulation results indicate better performance of TDE as compared to that of DE particularly for problems involving large number of control stages. [Copyright &y& Elsevier]

Convergence performance and optimization efficiency are two critical issues in the application of commonly used evolution algorithms in multi-objective design of wind turbines. A gradient-based multi-objective evolution algorithm is proposed for wind turbine blade design, based on uniform decomposition and positive-gradient differential evolution. In the uniform decomposition, uniformly distributed reference vectors are established in the objective space to maintain population diversity so that the population aggregations, which are commonly observed for wind turbine blade design using gradient-free algorithms, are minimized. The positive-gradient differential evolution is introduced for population evolution to increase optimization efficiency by guiding the evolutionary process and significantly reducing searching ranges of each individual. Two-, three- and four-objective optimizations of 1.5 MW wind turbine blades reveal that the proposed algorithm can deliver uniformly distributed optimal solutions in an efficient way, and has advantages over gradient-free algorithms in terms of convergence performance and optimization efficiency. These advantages increase with the optimization dimension, and the proposed algorithm is more suitable for optimizations of small size populations, thus remarkably enhancing the design efficiency. [ABSTRACT FROM AUTHOR]