Approximate and Widespread Pareto Solutions in the Structure-Control Design of Mechatronic Systems

The structure-control design approach of mechatronic systems requires a different design formulation where the mechanical structure and control system are simultaneously designed. Optimization problems are commonly stated to confront the structure-control design formulation. Nevertheless, these problems are often very complex with a highly nonlinear dependence between the design variables and performance functions. This fact has made the use of evolutionary algorithms, a feasible alternative to solve the highly nonlinear optimization problem; the method to find the best solution is an open issue in the structure-control design approach. Hence, this paper presents a mechanism to exhaustively exploit the solutions in the differential evolution (DE) algorithm in order to find more non-dominated solutions with uniformly distributed Pareto front and better trade-offs in the structure-control design framework. The proposed approach adopts an external population to retain the non-dominated solutions found during the evolutionary process and includes a mechanism to mutate the individuals in their corresponding external population region. As a study case, the structure-control design of a serial-parallel manipulator with its control system is stated as a dynamic optimization problem and is solved by using the proposed approach. A comparative analysis shows that the multi-objective exhaustive exploitation differential evolution obtained a superior performance in the structure-control design framework than a DE algorithm which did not consider the proposal. Hence, the resulting designs provide better trade-offs between the structure-control performance functions.     

A novel elitist multiobjective optimization algorithm: Multiobjective extremal optimization

Recently, a general-purpose local-search heuristic method called extremal optimization (EO) has been successfully applied to some NP-hard combinatorial optimization problems. This paper presents an investigation on EO with its application in numerical multiobjective optimization and proposes a new novel elitist (1 + λ) multiobjective algorithm, called multiobjective extremal optimization (MOEO). In order to extend EO to solve the multiobjective optimization problems, the Pareto dominance strategy is introduced to the fitness assignment of the proposed approach. We also present a new hybrid mutation operator that enhances the exploratory capabilities of our algorithm. The proposed approach is validated using five popular benchmark functions. The simulation results indicate that the proposed approach is highly competitive with the state-of-the-art multiobjective evolutionary algorithms. Thus MOEO can be considered a good alternative to solve numerical multiobjective optimization problems.     

A new metaheuristic algorithm based on shark smell optimization

In this article, a new metaheuristic optimization algorithm is introduced. This algorithm is based on the ability of shark, as a superior hunter in the nature, for finding prey, which is taken from the smell sense of shark and its movement to the odor source. Various behaviors of shark within the search environment, that is, sea water, are mathematically modeled within the proposed optimization approach. The effectiveness of the suggested approach is compared with many other heuristic optimization methods based on standard benchmark functions. Also, to illustrate the efficiency of the proposed optimization method for solving real‐world engineering problems, it is applied for the solution of load frequency control problem in electrical power systems. The obtained results confirm the validity of the proposed metaheuristic optimization algorithm. © 2014 Wiley Periodicals, Inc. Complexity 21: 97–116, 2016     

A genetic algorithm for designing microarray experiments

Heuristic techniques of optimization can be useful in designing complex experiments, such as microarray experiments. They have advantages over the traditional methods of optimization, particularly in situations where the search space is discrete. In this paper, a search procedure based on a genetic algorithm is proposed to find optimal (efficient) designs for both one- and multi-factor experiments. A genetic algorithm is a heuristic optimization method that exploits the biological evolution to obtain a solution of the problem. As an example, optimal designs for \(3\times 2\) factorial microarray experiments are presented for different numbers of arrays and for various sets of research questions. Comparisons between different operators of the genetic algorithm are performed by simulation studies.     

一种求解物流设施二次分配问题的混合分布估计算法

为了求解物流设施二次分配问题,提出了一种混合分布估计算法(HEDA)。首先,根据QAP的距离和物流量矩阵信息,提出了一种基于假设物流中心启发式规则的种群初始化方法,用于提高初始种群的质量和算法的搜索效率;其次,针对HEDA的概率模型,提出了一种概率矩阵初始构型生成机制和扰动操作,用于提高算法的全局探索能力;最后,在分析QAP的结构性质的基础上,设计了一种基于快速评价的局部搜索策略,用于提高算法的局部开发能力。仿真计算实验和算法比较验证了HEDA的优化性能。     

Managing pharmaceuticals delivery service using a hybrid particle swarm intelligence approach

Medicines or drugs have unique characteristics of short life cycle, small size, light weight, restrictive distribution time and the need of temperature and humidity control (selected items only). Thus, logistics companies often use different types of vehicles with different carrying capacities, and considering fixed and variable costs in service delivery, which make the vehicle assignment and route optimization more complicated. In this study, we formulate the problem to a multi-type vehicle assignment and mixed integer programming route optimization model with fixed fleet size under the constraints of distribution time and carrying capacity. Given non-deterministic polynomial hard and optimal algorithm can only be used to solve small-size problem, a hybrid particle swarm intelligence (PSI) heuristic approach, which adopts the crossover and mutation operators from genetic algorithm and 2-opt local search strategy, is proposed to solve the problem. We also adapt a principle based on cost network and Dijkstra’s algorithm for vehicle scheduling to balance the distribution time limit and the high loading rate. We verify the relative performance of the proposed method against several known optimal or heuristic solutions using a standard data set for heterogeneous fleet vehicle routing problem. Additionally, we compare the relative performance of our proposed Hybrid PSI algorithm with two intelligent-based algorithms, Hybrid Population Heuristic algorithm and Improved Genetic Algorithm, using a real-world data set to illustrate the practical and validity of the model and algorithm.

     

Multi-objective optimization of water-using systems

Industrial water systems often allow efficient water uses via water reuse and/or recirculation. The design of the network layout connecting water-using processes is a complex problem which involves several criteria to optimize. Most of the time, this design is achieved using Water Pinch technology, optimizing the freshwater flow rate entering the system. This paper describes an approach that considers two criteria: (i) the minimization of freshwater consumption and (ii) the minimization of the infrastructure cost required to build the network. The optimization model considers water reuse between operations and wastewater treatment as the main mechanisms to reduce freshwater consumption. The model is solved using multi-objective distributed Q-learning (MDQL), a heuristic approach based on the exploitation of knowledge acquired during the search process. MDQL has been previously tested on several multi-objective optimization benchmark problems with promising results [C. Mariano, Reinforcement learning in multi-objective optimization, Ph.D. thesis in Computer Science, Instituto Tecnológico y de Estudios Superiores de Monterrey, Campus Cuernavaca, March, 2002, Cuernavaca, Mor., México, 2001]. In order to compare the quality of the results obtained with MDQL, the reduced gradient method was applied to solve a weighted combination of the two objective functions used in the model. The proposed approach was tested on three cases: (i) a single contaminant four unitary operations problem where freshwater consumption is reduced via water reuse, (ii) a four contaminants real-world case with ten unitary operations, also with water reuse, and (iii) the water distribution network operation of Cuernavaca, Mexico, considering reduction of water leaks, operation of existing treatment plants at their design capacity, and design and construction of new treatment infrastructure to treat 100% of the wastewater produced. It is shown that the proposed approach can solved highly constrained real-world multi-objective optimization problems.     

Heuristic sensitivity analysis in a combinatoric environment: An exposition and case study

Sensitivity orpost-optimality analysis investigates the effect of parametric changes on heuristic robustness and solution quality. This approach is relatively unexplored for combinatorial optimization problems, and yet is of considerable interest in analyzing performance characteristics of heuristic approaches. The purpose of this paper is to: (1) develop the semantics and rationale of parametric analysis within the combinatoric environment; (2) present as an example the design and implementation of sensitivity analysis procedures for a newly developed heuristic — theVariable-Depth-Search Heuristic (VDSH) — to solve the Generalized Assignment Problem (GAP). The concepts and methodology discussed in this paper may as well be applied to other heuristics, or in developing a heuristic sensitivity analysis procedure for a large-scale optimization method.     

Adaptive Reservoir Evolutionary Algorithm: An Evolutionary On-Line Adaptation Scheme for Global Function Optimization

This paper introduces a novel global optimization heuristic algorithm based on the basic paradigms of Evolutionary Algorithms (EA). The algorithm greatly extends a previous strategy proposed by the authors in Munteanu and Lazarescu (1998). In the newly designed algorithm the exploration/exploitation of the search space is adapted on-line based on the current features of the landscape that is being searched. The on-line adaptation mechanism involves a decision process as to whether more exploitation or exploration is needed depending on the current progress of the algorithm and on the current estimated potential of discovering better solutions. The convergence with probability 1 in finite time and discrete space is analyzed, as well as an extensive comparison with other evolutionary optimization heuristics is performed on a set of test functions.     

Reducing costs of backhaul networks for PCS networks using genetic algorithms

Designing cost-effective telecommunications networks often involves solving several challenging, interdependent combinatorial optimization problems simultaneously. For example, it may be necessary to select a least-cost subset of locations (network nodes) to serve as hubs where traffic is to be aggregated and switched; optimally assign other nodes to these hubs, meaning that the traffic entering the network at these nodes will be routed to the assigned hubs while respecting capacity constraints on the links; and optimally choose the types of links to be used in interconnecting the nodes and hubs based on the capacities and costs associated with each link type. Each of these three combinatorial optimization problems must be solved while taking into account its impacts on the other two. This paper introduces a genetic algorithm (GA) approach that has proved effective in designing networks for carrying personal communications services (PCS) traffic. The key innovation is to represent information about hub locations and their interconnections as two parts of a chromosome, so that solutions to both aspects of the problem evolve in parallel toward a globally optimal solution. This approach allows realistic problems that take 4–10 hours to solve via a more conventional branch-and-bound heuristic to be solved in 30–35 seconds. Applied to a real network design problem provided as a test case by Cox California PCS, the heuristics successfully identified a design 10% less expensive than the best previously known design. Cox California PCS has adopted the heuristic results and plans to incorporate network optimization in its future network designs and requests for proposals.     

Optimization of aircraft structural components by using nature-inspired algorithms and multi-fidelity approximations

In this work, a flat pressure bulkhead reinforced by an array of beams is designed using a suite of heuristic optimization methods (Ant Colony Optimization, Genetic Algorithms, Particle Swarm Optimization and LifeCycle Optimization), and the Nelder-Mead simplex direct search method. The compromise between numerical performance and computational cost is addressed, calling for inexpensive, yet accurate analysis procedures. At this point, variable fidelity is proposed as a tradeoff solution. The difference between the low-fidelity and high-fidelity models at several points is used to fit a surrogate that corrects the low-fidelity model at other points. This allows faster linear analyses during the optimization; whilst a reduced set of expensive non-linear analyses are run “off-line,” enhancing the linear results according to the physics of the structure. Numerical results report the success of the proposed methodology when applied to aircraft structural components. The main conclusions of the work are (i) the variable fidelity approach enabled the use of intensive computing heuristic optimization techniques; and (ii) this framework succeeded in exploring the design space, providing good initial designs for classical optimization techniques. The final design is obtained when validating the candidate solutions issued from both heuristic and classical optimization. Then, the best design can be chosen by direct comparison of the high-fidelity responses.     

基于指数Laplace损失函数的回归估计鲁棒超限学习机

实际问题的数据集通常受到各种噪声的影响,超限学习机(extreme learning machine, ELM)对这类数据集进行学习时,表现出预测精度低、预测结果波动大.为了克服该缺陷,采用了能够削弱噪声影响的指数Laplace损失函数.该损失函数是建立在Gauss核函数基础上,具有可微、非凸、有界且能够趋近于Laplace函数的特点.将其引入到超限学习机中,提出了鲁棒超限学习机回归估计(exponential Laplace loss function based robust ELM for regression, ELRELM)模型.利用迭代重赋权算法求解模型的优化问题.在每次迭代中,噪声样本点被赋予较小的权值,能够有效地提高预测精度.真实数据集实验验证了所提出的模型相比较于对比算法具有更优的学习性能和鲁棒性.     

An ant colony system for enhanced loop-based aisle-network design

The purpose of this paper is to develop a global optimization model, simplification schemes, and a heuristic procedure for the design of a shortcut-enhanced unidirectional loop aisle-network with pick-up and drop-off stations. The objective is to minimize the total loaded and empty trip distances. This objective is the main determinant for the fleet size of the vehicles, which in turn is the driver of the total life-cycle cost of vehicle-based unit-load transport systems. The shortcut considerably reduces the length of the trips while maintaining the simplicity of the system. The global model solves simultaneously for the loop design, stations’ locations and shortcut design. We then develop two simplifications each containing two serial phases. Phase-1 of the first simplification step focuses on both loaded and empty trips, while that of the second simplification focuses only on loaded trips. In phase-2, both designs are enhanced with a shortcut to minimize both loaded and empty trip distances. The quality and efficiency of the three alternative designs are tested for a set of problems with different layout size and product mix. While the solution time of the second simplification procedure is a small percentage of the global formulation, it generates satisfactory solutions. On this foundation, we then develop a heuristic procedure to replace phase-1 of the second simplification. The heuristic procedure is using ant colony system to generate feasible solutions and then we implement a local search algorithm to improve the results. The heuristic algorithm quickly generates close to optimal solutions for phase-1 of the second simplification. By applying phase-2 of the this second simplification on a set of loops generated by the heuristic, close to optimal solutions are also quickly obtained for the global model.     

An effective krill herd algorithm with migration operator in biogeography-based optimization

Krill herd (KH) is a novel search heuristic method. To improve its performance, a biogeography-based krill herd (BBKH) algorithm is presented for solving complex optimization tasks. The improvement involves introducing a new krill migration (KM) operator when the krill updating to deal with optimization problems more efficiently. The KM operator emphasizes the exploitation and lets the krill cluster around the best solutions at the later run phase of the search. The effects of these enhancements are tested by various well-defined benchmark functions. Based on the experimental results, this novel BBKH approach performs better than the basic KH and other optimization algorithms.     

Tree search for the stacking problem

The stacking problem is a hard combinatorial optimization problem with high practical interest in, for example, steel storage or container port operations. In this problem, a set of items is stored in a warehouse for a period of time, and a crane is used to place them in a limited number of stacks. Since the entrance and exit of items occurs in an arbitrary order, items may have to be relocated in order to reach and deliver other items below them. The objective of the problem is to find a feasible sequence of movements that delivers all items, while minimizing the total number of movements. We study the scalability of an exact approach to this problem, and propose two heuristic methods to solve it approximately. The two heuristic approaches are a multiple simulation algorithm using semi-greedy construction heuristics, and a stochastic best-first tree search algorithm. The two methods are compared in a set of challenging instances, revealing a superior performance of the tree search approach in most cases.     

PSOHS: an efficient two-stage approach for data clustering

Cluster analysis is an important task in data mining and refers to group a set of objects such that the similarities among objects within the same group are maximal while similarities among objects from different groups are minimal. The particle swarm optimization algorithm (PSO) is one of the famous metaheuristic optimization algorithms, which has been successfully applied to solve the clustering problem. However, it has two major shortcomings. The PSO algorithm converges rapidly during the initial stages of the search process, but near global optimum, the convergence speed will become very slow. Moreover, it may get trapped in local optimum if the global best and local best values are equal to the particle’s position over a certain number of iterations. In this paper we hybridized the PSO with a heuristic search algorithm to overcome the shortcomings of the PSO algorithm. In the proposed algorithm, called PSOHS, the particle swarm optimization is used to produce an initial solution to the clustering problem and then a heuristic search algorithm is applied to improve the quality of this solution by searching around it. The superiority of the proposed PSOHS clustering method, as compared to other popular methods for clustering problem is established for seven benchmark and real datasets including Iris, Wine, Crude Oil, Cancer, CMC, Glass and Vowel.     

Inverse integer optimization with an imperfect observation

This study develops and evaluates methods for inverse integer optimization problems with an imperfect observation where the unknown parameters are the cost coefficients. We propose a cutting plane algorithm for this problem and compare it to a heuristic which solves the inverse of the linear relaxation of the forward problem. We then propose a hybrid approach that initializes the cutting plane algorithm from the solution of the heuristic.     

最小化总反馈长度的耦合活动排程研究

耦合活动的排程直接影响新产品开发的周期和成本,因而受到了学者和研发管理人员的普遍关注。本文针对最小化总反馈长度这一耦合活动排程常用目标,将遗传算法与局部搜索算法相结合,提出了一种新的混合优化算法,并系统分析了参数对算法性能的影响。然后将算法应用到实际案例和大量随机算例中,实验结果表明混合优化算法较大幅度提高了现有局部搜索算法解的质量;同等情形下,混合优化算法所获得解比单纯运用遗传算法所获得解更好。     

Optimal Noncollapsing Space-Filling Designs for Irregular Experimental Regions

Space-filling and noncollapsing are two important properties in designing computer experiments. We study how the noncollapsing, space-filling designs for irregular experimental regions can be generated efficiently by the proposed metaheuristic methods. We solve this optimal design problem using variants of the discrete particle swarm optimization (DPSO) approaches. Numerical results, including an application in data center thermal management, are used to illustrate the performances of the proposed algorithms. Based on these numerical results, we assert that the most efficient approach is to reformulate the target optimal design problem as a constrained optimization problem and then use a modified DPSO to solve the constrained optimization problem.     

Global optimization based on novel heuristics,low-discrepancy sequences and genetic algorithms

In this paper a new heuristic hybrid technique for bound-constrained global optimization is proposed. We developed iterative algorithm called GLP_τS that uses genetic algorithms, LP_τ low-discrepancy sequences of points and heuristic rules to find regions of attraction when searching a global minimum of an objective function. Subsequently Nelder–Mead Simplex local search technique is used to refine the solution. The combination of the three techniques (Genetic algorithms, LP_τO Low-discrepancy search and Simplex search) provides a powerful hybrid heuristic optimization method which is tested on a number of benchmark multimodal functions with 10–150 dimensions, and the method properties – applicability, convergence, consistency and stability are discussed in detail.