An optimization algorithm for pulse-frequency modulated control systems

A technique is presented for optimizing open-loop plants driven by PFM inputs. Ft. determines both the optimal amplitude and time of occurrence of the input pulses. It can be applied to linear or non-linear plants, to a large class of objective functionst to any number of input pulses, and for fixed or free end times. A step-by-step algorithm and a numerical example are given to demonstrate the technique.     

An expert system for paperwork optimization in production systems

Much like material handling, process paperwork only contributes to overhead cost (not proportionally included in the final product). The very computing technologies that have produced electronic mail systems (and similar paper-less information systems) have also made it quite easy and tempting to generate excessive paperwork in form of memos, reports, charts, lists, files, etc. This paper describes an expert system for minimizing such paperwork activities in various production systems.     

An ordinal optimization theory-based algorithm for large distributed power systems

In this paper, we propose an ordinal optimization (OO) theory-based algorithm to solve the yet to be explored distributed state estimation with continuous and discrete variables problems (DSECDP) of large distributed power systems. The proposed algorithm copes with a huge amount of computational complexity problem in large distributed systems and obtains a satisfactory solution with high probability based on the OO theory. There are two contributions made in this paper. First, we have developed an OO theory-based algorithm for DSECDP in a deregulated environment. Second, the proposed algorithm is implemented in a distributed power system to select a good enough discrete variable solution. We have tested the proposed algorithm for numerous examples on the IEEE 118-bus and 244-bus with four subsystems using a 4-PC network and compared the results with other competing approaches: Genetic Algorithm, Tabu Search, Ant Colony System and Simulated Annealing methods. The test results demonstrate the validity, robustness and excellent computational efficiency of the proposed algorithm in obtaining a good enough feasible solution.     

An optimization algorithm inspired by social creativity systems

The need for efficient and effective optimization problem solving methods arouses nowadays the design and development of new heuristic algorithms. This paper present ideas that leads to a novel multiagent metaheuristic technique based on creative social systems suported on music composition concepts. This technique, called “Musical Composition Method” (MMC), which was proposed in Mora-Gutiérrez et?al. (Artif Intell Rev 2012) as well as a variant, are presented in this study. The performance of MMC is evaluated and analyzed over forty instances drawn from twenty-two benchmark global optimization problems. The solutions obtained by the MMC algorithm were compared with those of various versions of particle swarm optimizer and harmony search on the same problem set. The experimental results demonstrate that MMC significantly improves the global performances of the other tested metaheuristics on this set of multimodal functions.     

An efficient cluster-based tribes optimization algorithm for functional-link-based neurofuzzy inference systems

This study presents an efficient cluster-based tribes optimization algorithm (CTOA) for designing a functional-link-based neurofuzzy inference system (FLNIS) for prediction applications. The proposed CTOA learning algorithm was used to optimize the parameters of the FLNIS model. The proposed CTOA adopts a self-clustering algorithm to divide the swarm into multiple tribes, and uses different displacement strategies to update each particle. The CTOA also uses a tribal adaptation mechanism to generate or remove particles and reconstruct tribal links. The tribal adaptation mechanism can improve the quality of the tribe and the tribe adaptation. In CTOA, the displacement strategy and the tribal adaptation mechanism depend on the tribal leaders to strengthen the local search ability. Finally, the proposed FLNIS-CTOA method was applied to several prediction problems. The results of this study demonstrate the effectiveness of the proposed CTOA learning algorithm.     

An effective hybrid biogeography-based optimization algorithm for parameter estimation of chaotic systems

Parameter estimation of chaotic systems is an important issue in the fields of computational mathematics and nonlinear science, which has gained increasing research and applications. In this paper, biogeography-based optimization (BBO), a new effective optimization algorithm based on the biogeography theory of the geographical distribution of biological organisms, is reasonably combined with differential evolution and simplex search to develop an effective hybrid algorithm for solving parameter estimation problem that is formulated as a multi-dimensional optimization problem. By suitably fusing several optimization methods with different searching mechanisms and features, the exploration and exploitation abilities of the hybrid algorithm can be enhanced and well balanced. Numerical simulation based on several typical chaotic systems and comparisons with some existing methods demonstrate the effectiveness of the proposed algorithm. In addition, the effects of population size and noise on the performances of the hybrid algorithm are investigated.     

Potential offspring production strategies: An improved genetic algorithm for global numerical optimization

In this paper, a sharing evolution genetic algorithms (SEGA) is proposed to solve various global numerical optimization problems. The SEGA employs a proposed population manager to preserve chromosomes which are superior and to eliminate those which are worse. The population manager also incorporates additional potential chromosomes to assist the solution exploration, controlled by the current solution searching status. The SEGA also uses the proposed sharing concepts for cross-over and mutation to prevent populations from falling into the local minimal, and allows GA to easier find or approach the global optimal solution. All the three parts in SEGA, including population manager, sharing cross-over and sharing mutation, can effective increase new born offspring’s solution searching ability. Experiments were conducted on CEC-05 benchmark problems which included unimodal, multi-modal, expanded, and hybrid composition functions. The results showed that the SEGA displayed better performance when solving these benchmark problems compared to recent variants of the genetic algorithms.     

An interval algorithm for multi-objective optimization

This paper presents an interval algorithm for solving multi-objective optimization problems. Similar to other interval optimization techniques, [see Hansen and Walster (2004)], the interval algorithm presented here is guaranteed to capture all solutions, namely all points on the Pareto front. This algorithm is a hybrid method consisting of local gradient-based and global direct comparison components. A series of example problems covering convex, nonconvex, and multimodal Pareto fronts is used to demonstrate the method.     

Matrix-free algorithm for the optimization of multidisciplinary systems

Multidisciplinary engineering systems are usually modeled by coupling software components that were developed for each discipline independently. The use of disparate solvers complicates the optimization of multidisciplinary systems and has been a long-standing motivation for optimization architectures that support modularity. The individual discipline feasible (IDF) formulation is particularly attractive in this respect. IDF achieves modularity by introducing optimization variables and constraints that effectively decouple the disciplinary solvers during each optimization iteration. Unfortunately, the number of variables and constraints can be significant, and the IDF constraint Jacobian required by most conventional optimization algorithms is prohibitively expensive to compute. Furthermore, limited-memory quasi-Newton approximations, commonly used for large-scale problems, exhibit linear convergence rates that can struggle with the large number of design variables introduced by the IDF formulation. In this work, we show that these challenges can be overcome using a reduced-space inexact-Newton-Krylov algorithm. The proposed algorithm avoids the need for the explicit constraint Jacobian and Hessian by using a Krylov iterative method to solve the Newton steps. The Krylov method requires matrix-vector products, which can be evaluated in a matrix-free manner using second-order adjoints. The Krylov method also needs to be preconditioned, and a key contribution of this work is a novel and effective preconditioner that is based on approximating a monolithic solution of the (linearized) multidisciplinary system. We demonstrate the efficacy of the algorithm by comparing it with the popular multidisciplinary feasible formulation on two test problems.     

An efficient algorithm for high-dimensional function optimization

To solve high-dimensional function optimization problems, many evolutionary algorithms have been proposed. In this paper, we propose a new cooperative coevolution orthogonal artificial bee colony (CCOABC) algorithm in an attempt to address the issue effectively. Cooperative coevolution frame, a popular technique in evolutionary algorithms for large scale optimization problems, is adopted in this paper. This frame decomposes the problem into several subcomponents by random grouping, which is a novel decomposition strategy mainly for tackling nonseparable functions. This strategy can increase the probability of grouping interacting variables in one subcomponent. And for each subcomponent, an improved artificial bee colony (ABC) algorithm, orthogonal ABC, is employed as the subcomponent optimizer. In orthogonal ABC, an Orthogonal Experimental Design method is used to let ABC evolve in a quick and efficient way. The algorithm has been evaluated on standard high-dimensional benchmark functions. Compared with other four state-of-art evolutionary algorithms, the simulation results demonstrate that CCOABC is a highly competitive algorithm for solving high-dimensional function optimization problems.     

An interior-point QP algorithm for structural optimization

In this paper, a new algorithm for convex quadratic programming (QP) is presented. Firstly, the surrogate problem for QP is developed, and the Karush-Kuhn-Tucker conditions of the surrogate problem hold if the unconstrained minimum of the objective function does not satisfy any constraints. Then, Karmarkar's algorithm for linear programming (LP) is introduced to solve the surrogate dual problem. In addition, the case of general constraints is also discussed, and some examples of optimum truss sizing problems show that the proposed algorithm is robust and efficient.     

An algorithm comparison for dynamic optimization problems

This work presents a study on the performance of several algorithms on different continuous dynamic optimization problems. Eight algorithms have been used: SORIGA (an Evolutionary Algorithm), an agents-based algorithm, the mQSO (a widely used multi-population PSO) as well as three heuristic-rule-based variations of it, and two trajectory-based cooperative strategies. The algorithms have been tested on the Moving Peaks Benchmark and the dynamic version of the Ackley, Griewank and Rastrigin functions. For each problem, a wide variety of configuration variations have been used, emphasizing the influence of dynamism, and using a full-factorial experimental design. The results give an interesting overview of the properties of the algorithms and their applicability, and provide useful hints to face new problems of this type with the best algorithmic approach. Additionally, a recently introduced methodology for comparing a high number of experimental results in a graphical way is used.     

分布式系统TTCAN调度优化算法研究

总线技术的发展给线缆测试仪带来了分布式、信息化、网络化的新需求,且在分布式线缆测试仪工作过程中,测试线路的数目增加也对总线数据通讯的稳定性和通讯效率提出了更高的要求;针对分布式系统在线缆测试中的应用需要,设计并优化了分布式线缆测试仪工作的TTCAN应用层协议和其系统矩阵;对于分布式系统通信中的周期性消息形成的系统矩阵先后采用遗传算法、改进型差分进化算法进行优化,对于其中的非周期性消息采用基于松弛度的动态优先级算法;在MATLAB仿真环境中进行实验,实验结果表明,改进型差分算法比遗传算法能够更快、更稳定地计算出优化矩阵,经调度优化后的TTCAN总线工作时数据传输效率有显著提高;文章通过智能优化算法,有效提高了系统总线的通讯效率和稳定性。     

Computational algorithm for input-signal optimization in distributed-parameter systems identification

This paper deals with the problem of optimum choice of testing signals for identification in linear distributed-parameter systems. It is based on earlier theoretical results of one of the authors, which are extended here by investigating the structure of the optimal input signal. These investigations allow the proposal of an efficient computational algorithm. Its convergence is proved and the convergence rate is verified by numerical studies     

An operational optimization procedure for production scheduling

The methodology takes a sufficiently long time horizon and breaks the problem into two subproblems. The first subproblem is the long range planning model and the second the short run production scheduling model. The long range model is essentially a resource constrained model and has a linear programming formulation with a profit maximization objective function. The long range plan fixes the discretionary marketing variables, such as the selection of product line, and the timing and extent of promotional sales. It estimates manpower requirements and establishes the raw material procurement plans. Lagrange multipliers obtained in the long range model are then used in the short run production scheduling model. The scheduling algorithm, having a Lagrangian function for an objective, is the solution to an unconstrained maximization problem. This then reduces to one of sequential allocation of production facilities to products. The algorithm is being applied on a problem with five production lines, 126 products, 26 time periods and 32 raw material constraints.     

生化系统稳态优化的一种新算法

针对一类生化系统的稳态优化问题, 在已有间接优化方法(IOM)的线性优化问题中引入一个反映S–系统解和原模型解一致性的等式约束, 应用Lagrangian乘子法将修正后的非线性优化问题转化为一个等价的线性优化问题, 提出了一种改进的稳态优化新算法. 该优化算法不仅可以收敛到正确的系统最优解, 而且可用现有的线性规划算法去计算. 最后将算法应用于几个生化系统的稳态优化中, 结果表明, 本文提出的优化算法是有效的.     

An improved teaching-learning-based optimization algorithm for numerical and engineering optimization problems

The teaching-learning-based optimization (TLBO) algorithm, one of the recently proposed population-based algorithms, simulates the teaching-learning process in the classroom. This study proposes an improved TLBO (ITLBO), in which a feedback phase, mutation crossover operation of differential evolution (DE) algorithms, and chaotic perturbation mechanism are incorporated to significantly improve the performance of the algorithm. The feedback phase is used to enhance the learning style of the students and to promote the exploration capacity of the TLBO. The mutation crossover operation of DE is introduced to increase population diversity and to prevent premature convergence. The chaotic perturbation mechanism is used to ensure that the algorithm can escape the local optimal. Simulation results based on ten unconstrained benchmark problems and five constrained engineering design problems show that the ITLBO algorithm is better than, or at least comparable to, other state-of-the-art algorithms.     

一种改进的协同优化算法

针对协同优化过程对初始点敏感以及容易陷入局部最优点的问题,提出了一种改进的协同优化算法。改进后的协同优化算法综合考虑学科级优化设计点与系统级设计点的距离以及子学科级内部最优设计点,能较好地减弱优化结果对初始点以及松弛因子选择的依赖性,更容易找到优化问题全局最优设计点。最后,通过两个经典算例验证了改进算法的有效性及稳定性。     

An enhanced pathfinder algorithm for engineering optimization problems

The pathfinder algorithm (PFA) is a new population-based optimizer, it divides the search agents of the algorithm into leaders and followers, imitating the leadership level of the group movement to find the best food area or prey. In PFA, followers follow the new position according to the position of the leader and their own consciousness makes the algorithm easy to fall into local optimum. To overcome this shortcoming, the following stage is complicated in this paper, and the acceptance operator, the exchange operator and the mutation mechanism are introduced into the algorithm. To further balance the mining ability and exploration ability of the algorithm, the article regards the leader as a guide and introduces a guide mechanism. To verify the performance of the improved algorithm, it is applied to nine real-life engineering case problems. The simulation results of the real-life engineering design problems exhibit the superiority of the improved PFA (IMPFA) algorithm in solving challenging problems with constrained and unknown search spaces when compared to the basic PFA algorithm or other available solutions.

     

一种改进的混沌优化算法

为了克服遗传算法的早熟现象以及混沌优化的搜索时间过长的缺点,将遗传算法、混沌优化和变尺度方法相结合,提出了一种改进的混沌优化算法.该算法利用混沌的随机性、遍历性和规律性来避免陷入局部极小值,从而也克服了遗传算法中的早熟现象,同时引入了变尺度方法提高该算法的搜索速度.本文还给出了算法的收敛性分析.对典型测试函数的仿真结果表明此算法优于变尺度混沌优化和遗传算法.