Multi-objective reservoir operation using particle swarm optimization with adaptive random inertia weights

Based on conventional particle swarm optimization(PSO),this paper presents an efficient and reliable heuristic approach using PSO with an adaptive random inertia weight(ARIW)strategy,referred to as the ARIW-PSO algorithm,to build a multi-objective optimization model for reservoir operation.Using the triangular probability density function,the inertia weight is randomly generated,and the probability density function is automatically adjusted to make the inertia weight generally greater in the initial stage of evolution,which is suitable for global searches.In the evolution process,the inertia weight gradually decreases,which is beneficial to local searches.The performance of the ARIWPSO algorithm was investigated with some classical test functions,and the results were compared with those of the genetic algorithm(GA),the conventional PSO,and other improved PSO methods.Then,the ARIW-PSO algorithm was applied to multi-objective optimal dispatch of the Panjiakou Reservoir and multi-objective flood control operation of a reservoir group on the Luanhe River in China,including the Panjiakou Reservoir,Daheiting Reservoir,and Taolinkou Reservoir.The validity of the multi-objective optimization model for multi-reservoir systems based on the ARIW-PSO algorithm was verified.

Optimization of QoS Parameters in Cognitive Radio Using Combination of Two Crossover Methods in Genetic Algorithm

Radio Cognitive (RC) is the new concept introduced to improve spectrum utilization in wireless communication and present important research field to resolve the spectrum scarcity problem. The powerful ability of CR to change and adapt its transmit parameters according to environmental sensed parameters, makes CR as the leading technology to manage spectrum allocation and respond to QoS provisioning. In this paper, we assume that the radio environment has been sensed and that the SU specifies QoS requirements of the wireless application. We use genetic algorithm (GA) and propose crossover method called Combined Single-Heuristic Crossover. The weighted sum multi-objective approach is used to combine performance objectives functions discussed in this paper and BER approximate formula is considered.

Multi-objective optimal design of braced frames using hybrid genetic and ant colony optimization

In this article, multi-objective optimization of braced frames is investigated using a novel hybrid algorithm. Initially, the applied evolutionary algorithms, ant colony optimization （ACO） and genetic algorithm （GA） are reviewed, followed by developing the hybrid method. A dynamic hybridization of GA and ACO is proposed as a novel hybrid method which does not appear in the literature for optimal design of steel braced frames. Not only the cross section of the beams, columns and braces are considered to be the design variables, but also the topologies of the braces are taken into account as additional design variables. The hybrid algorithm explores the whole design space for optimum solutions. Weight and maximum displacement of the structure are employed as the objective functions for multi-objective optimal design. Subsequently, using the weighted sum method （WSM）, the two objective problem are converted to a single objective optimization problem and the proposed hybrid genetic ant colony algorithm （HGAC） is developed for optimal design. Assuming different combination for weight coefficients, a trade-offbetween the two objectives are obtained in the numerical example section. To make the final decision easier for designers, related constraint is applied to obtain practical topologies. The achieved results show the capability of HGAC to find optimal topologies and sections for the elements.

基于随机权重多目标遗传算法的多目标动态单元构建方法

考虑多变的市场需求环境下单元生产系统在多个计划期具有多个目标的动态构建决策问题。通过对单元生产构建过程中的总费用、设备负载与能力之间最大偏差以及零部件跨单元移动的总次数3个目标进行权衡,建立了非线性多目标动态单元构建的数学模型。采用自适应小生境技术、惩罚技术、双轮盘赌法和精华选择策略,提出了基于精华保留策略的随机权重多目标遗传算法求解该组合优化问题。结合实例对模型和算法进行了仿真分析,结果显示了算法对解决多目标动态单元构建问题的有效性。

一种“源-网-荷-储”协同优化调度模型

为在配电网调度中使每种资源的价值最大化,建立一种基于双层模型的“源-网-荷-储”协同的配电网优化调度策略,通过上下层目标的相互配合达到最终优化效果。首先,主要考虑分布式风电、分布式光伏在调动配电网中的可控负载和储能配合清洁能源出力的优化调度。其次,以最低年度综合成本为模型的上层优化目标,以负荷变化量最优为下层优化目标,采用实时优化的协调优化策略。最后,结合遗传随机权重粒子群算法求解优化模型,通过对IEEE 33节点系统进行仿真分析。仿真结果表明,基于“源-网-荷-储”协同的配电网双层优化模型是一种更有效且更经济的调度模型。

商用车驾驶室-座椅悬置系统振动性能研究

文中将驾驶室、座椅悬置视为一个系统,构建驾驶室-座椅悬置系统4自由度动力学模型,采用拉格朗日方程建立悬置系统的振动方程,结合振动方程编写程序代码,分析了悬置系统的固有频率、主振型和频响函数。在此基础上,采用整车道路采样测试技术及台架载荷复现迭代技术得到比利时路和一般公路的激励信号,基于随机振动理论编写程序,研究了比利时路随机激励下的振动响应,依据国标规范进行人体振动舒适性评价;然后,根据悬置系统设计要求,提出了悬置系统的优化匹配设计方法,采用Isight软件中多岛遗传算法对悬置系统参数进行优化,获得了悬置系统设计参数最优解;最后,研究了优化前后的悬置系统参数在一般公路激励下的隔振效果。计算结果表明:新的悬置系统隔振性能更加优良,极大地提升了舒适性。