一种基于并行混沌和SFLA的风电电网无功优化模型设计

针对已有方法在解决电网无功优化时,由于系统的无功不足和电网电压的不稳定,容易过早收敛到局部最优解的缺点,设计了一种基于并行混沌和混合蛙跳算法 (Shuffle Frog Leaping Algorithm, SFLA)的电网无功优化模型。首先,建立了最小化有功网损、最大化静态电压稳定裕度和最大化无功补偿单位投资收益的多目标数学优化模型,然后,对经典的SFLA进行改进,通过引入精英协同进化机制和划分种群的方式实现并行寻优,从而增加个体的多样性和加快最优解的求取速度,在不同种群中设计不同的适应度函数和个体更新进化方法。为了使得算法的初始解分布更为均匀,引入用混沌机制来对种群进行初始化,最后,对基于并行混沌和SFLA的总体算法进行了设计和分析。在Matlab环境下进行实验,实验结果表明文中方法得到的优化结果具有电网有功损耗小、单位投资收益高和静态电压稳定裕度大的优点,具有较强的可行性和适应性。     

On the limitations of classical benchmark functions for evaluating robustness of evolutionary algorithms

Although evolutionary algorithms (EAs) have some operators which let them explore the whole search domain, still they get trapped in local minima when multimodality of the objective function is increased. To improve the performance of EAs, many optimization techniques or operators have been introduced in recent years. However, it seems that these modified versions exploit some special properties of the classical multimodal benchmark functions, some of which have been noted in previous research and solutions to eliminate them have been proposed.In this article, we show that quite symmetric behavior of the available multimodal test functions is another example of these special properties which can be exploited by some EAs such as covariance matrix adaptation evolution strategy (CMA-ES). This method, based on its invariance properties and good optimization results for available unimodal and multimodal benchmark functions, is considered as a robust and efficient method. However, as far as black box optimization problems are considered, no special trend in the behavior of the objective function can be assumed; consequently this symmetry limits the generalization of optimization results from available multimodal benchmark functions to real world problems. To improve the performance of CMA-ES, the Elite search sub-algorithm is introduced and implemented in the basic algorithm. Importance and effect of this modification is illustrated experimentally by dissolving some test problems in the end.     

基于HGA空间物资存储任务规划方法

针对物流领域物资存储任务规划问题进行研究。本文通过遗传算法（GA）结合启发式规则的思想,得到了在物资存储方面实用性较强的混合遗传算法（HGA）。该算法具备GA的优越性,并基于启发式规则对染色体信息及其组合进行优化和限定,依靠遗传算法的精英保留策略,避免了传统遗传算法常见的早熟收敛。仿真结果表明,该算法所得到的规划方法将不断逼近最优解,这就为三维空间物资存储任务规划提供合理方案,能显著提高效率。     

Modified particle swarm optimization algorithm with simulated annealing behavior and its numerical verification

The hybrid algorithm that combined particle swarm optimization with simulated annealing behavior (SA-PSO) is proposed in this paper. The SA-PSO algorithm takes both of the advantages of good solution quality in simulated annealing and fast searching ability in particle swarm optimization. As stochastic optimization algorithms are sensitive to their parameters, proper procedure for parameters selection is introduced in this paper to improve solution quality. To verify the usability and effectiveness of the proposed algorithm, simulations are performed using 20 different mathematical optimization functions with different dimensions. The comparative works have also been conducted among different algorithms under the criteria of quality of the solution, the efficiency of searching for the solution and the convergence characteristics. According to the results, the SA-PSO could have higher efficiency, better quality and faster convergence speed than compared algorithms.