JAMES: An object‐oriented Java framework for discrete optimization using local search metaheuristics

This paper describes the Java Metaheuristics Search framework (JAMES, v1.1): an object‐oriented Java framework for discrete optimization using local search algorithms that exploits the generality of such metaheuristics by clearly separating search implementation and application from problem specification. A wide range of generic local searches are provided, including (stochastic) hill climbing, tabu search, variable neighbourhood search and parallel tempering. These can be applied to any user‐defined problem by plugging in a custom neighbourhood for the corresponding solution type. Using an automated analysis workflow, the performance of different search algorithms can be compared in order to select an appropriate optimization strategy. Implementations of specific components are included for subset selection, such as a predefined solution type, generic problem definition and several subset neighbourhoods used to modify the set of selected items. Additional components for other types of problems (e.g. permutation problems) are provided through an extensions module which also includes the analysis workflow. In comparison with existing Java metaheuristics frameworks that mainly focus on population‐based algorithms, JAMES has a much lower memory footprint and promotes efficient application of local searches by taking full advantage of move‐based evaluation. Releases of JAMES are deployed to the Maven Central Repository so that the framework can easily be included as a dependency in other Java applications. The project is fully open source and hosted on GitHub. More information can be found at http://www.jamesframework.org . Copyright © 2016 John Wiley & Sons, Ltd.     

Meta‐heuristic multi‐ and many‐objective optimization techniques for solution of machine learning problems

Recently, multi‐ and many‐objective meta‐heuristic algorithms have received considerable attention due to their capability to solve optimization problems that require more than one fitness function. This paper presents a comprehensive study of these techniques applied in the context of machine learning problems. Three different topics are reviewed in this work: (a) feature extraction and selection, (b) hyper‐parameter optimization and model selection in the context of supervised learning, and (c) clustering or unsupervised learning. The survey also highlights future research towards related areas.     

Multi‐objective memetic algorithm: comparing artificial neural networks and pattern search filter method approaches

In this work, two methodologies to reduce the computation time of expensive multi‐objective optimization problems are compared. These methodologies consist of the hybridization of a multi‐objective evolutionary algorithm (MOEA) with local search procedures. First, an inverse artificial neural network proposed previously, consisting of mapping the decision variables into the multiple objectives to be optimized in order to generate improved solutions on certain generations of the MOEA, is presented. Second, a new approach based on a pattern search filter method is proposed in order to perform a local search around certain solutions selected previously from the Pareto frontier. The results obtained, by the application of both methodologies to difficult test problems, indicate a good performance of the approaches proposed.     

Multi‐objective design optimization of antennas for reflection,size, and gain variability using kriging surrogates and generalized domain segmentation

Cost‐efficient multi‐objective design optimization of antennas is presented. The framework exploits auxiliary data‐driven surrogates, a multi‐objective evolutionary algorithm for initial Pareto front identification, response correction techniques for design refinement, as well as generalized domain segmentation. The purpose of this last mechanism is to reduce the volume of the design space region that needs to be sampled in order to construct the surrogate model, and, consequently, limit the number of training data points required. The recently introduced segmentation concept is generalized here to allow for handling an arbitrary number of design objectives. Its operation is illustrated using an ultra‐wideband monopole optimized for best in‐band reflection, minimum gain variability, and minimum size. When compared with conventional surrogate‐based approach, segmentation leads to reduction of the initial Pareto identification cost by over 20%. Numerical results are supported by experimental validation of the selected Pareto‐optimal antenna designs.     

Multi‐criteria IoT resource discovery: a comparative analysis

The growth of real‐world objects with embedded and globally networked sensors allows to consolidate the Internet of things paradigm and increase the number of applications in the domains of ubiquitous and context‐aware computing. The merging between cloud computing and Internet of things named cloud of things will be the key to handle thousands of sensors and their data. One of the main challenges in the cloud of things is context‐aware sensor search and selection. Typically, sensors require to be searched using two or more conflicting context properties. Most of the existing work uses some kind of multi‐criteria decision analysis to perform the sensor search and selection, but does not show any concern for the quality of the selection presented by these methods. In this paper, we analyse the behaviour of the SAW, TOPSIS and VIKOR multi‐objective decision methods and their quality of selection comparing them with the Pareto‐optimality solutions. The gathered results allow to analyse and compare these algorithms regarding their behaviour, the number of optimal solutions and redundancy. Copyright © 2016 John Wiley & Sons, Ltd.     

Multi‐objective state feedback control for linear delay systems

This paper provides new linear matrix inequalities (LMI)‐based formulae for mixed H₂/H_∞ state‐feedback synthesis of linear continuous‐time systems with state delays of any size. The proposed delay‐independent LMI‐based conditions enable us to parameterize a memoryless state‐feedback controller without involving the Lyapunov variables in the formula. Compared with previous results based on a common Lyapunov variable, the proposed formula provides less conservative results. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society     

MRMOGA: a new parallel multi‐objective evolutionary algorithm based on the use of multiple resolutions

In this paper, we introduce MRMOGA (Multiple Resolution Multi‐Objective Genetic Algorithm), a new parallel multi‐objective evolutionary algorithm which is based on an injection island approach. This approach is characterized by adopting an encoding of solutions which uses a different resolution for each island. This approach allows us to divide the decision variable space into well‐defined overlapped regions to achieve an efficient use of multiple processors. Also, this approach guarantees that the processors only generate solutions within their assigned region. In order to assess the performance of our proposed approach, we compare it to a parallel version of an algorithm that is representative of the state‐of‐the‐art in the area, using standard test functions and performance measures reported in the specialized literature. Our results indicate that our proposed approach is a viable alternative to solve multi‐objective optimization problems in parallel, particularly when dealing with large search spaces. Copyright © 2006 John Wiley & Sons, Ltd.     

Multi‐organization scheduling approximation algorithms

In this paper we consider the problem of scheduling on computing platforms composed of several independent organizations, known as the Multi‐Organization Scheduling Problem (MOSP). Each organization provides both resources and jobs and follows its own objectives. We are interested in the best way to minimize the makespan on the entire platform when the organizations behave in a selfish way. We study the complexity of the MOSP problem with two different local objectives—makespan and average completion time—and show that MOSP is strongly NP‐Hard in both cases. We formally define a selfishness notion, by means of restrictions on the schedules. We prove that selfish behavior imposes a lower bound of 2 on the approximation ratio for the global makespan. We present various approximation algorithms of ratio 2 which validate selfishness restrictions. These algorithms are experimentally evaluated through simulation, exhibiting good average performances and presenting good fairness to organizations' local objectives. Copyright © 2011 John Wiley & Sons, Ltd.     

Multi‐objective optimal motion control of a laboratory helicopter based on parallel simple cell mapping method

This paper presents a study of multi‐objective optimal design of nonlinear control systems and has validated the control design with a twin rotor model helicopter. The gains of the porportional integral differential (PID) control are designed in the framework of multi‐objective opitmization. Eight design paramaters are optimized to minimize six time‐domain objective objective functions. The study of multi‐objective optimal design of feedback control with such a number of design paramaters and objective functions is rare in the literature. The Pareto optimal solutions are obtained by the proposed parallel simple cell mapping method consisting of a robust Pareto set recovery algorithm and a rolling subdivision technique. The proposed parallel simple cell mapping algorithm has two features: the number of cells in the invariant set grows linearly with the rolling subdivisions, and the Pareto set is insensitive to the inital set of seed cells. The current control design is compared with the classical LQE control for linear systems, and is also experimentally validated. The current design provides improved control performance as compares with the LQR control, and is applicable to complex nonlinear systems.     

Enhanced genetic algorithms for a bi‐objective bus driver rostering problem: a computational study

In this work, the bus driver rostering problem is considered in the context of a noncyclic rostering, with two objectives representing either the company or the drivers’ interests. A network model and a proof of the NP‐hardness of the problem are presented, along with a bi‐objective memetic algorithm that combines a specific decoder with a utopian/lexicographic elitism, a strength Pareto fitness evaluation, and a local search procedure. By taking real and benchmark instances the computational behavior of the memetic algorithm is compared with simpler versions to assess the effects of the embedded components. The developed algorithm is a valuable tool for bus companies’ planning departments insofar as it yields at low computing times a pool of good quality rosters that reconcile contradictory objectives. This study shows that simple enhancements in standard bi‐objective genetic algorithms may improve the results for this difficult combinatorial problem.     

Evolutionary algorithms for the multi‐objective test data generation problem

Automatic test data generation is a very popular domain in the field of search‐based software engineering. Traditionally, the main goal has been to maximize coverage. However, other objectives can be defined, such as the oracle cost, which is the cost of executing the entire test suite and the cost of checking the system behavior. Indeed, in very large software systems, the cost spent to test the system can be an issue, and then it makes sense by considering two conflicting objectives: maximizing the coverage and minimizing the oracle cost. This is what we did in this paper. We mainly compared two approaches to deal with the multi‐objective test data generation problem: a direct multi‐objective approach and a combination of a mono‐objective algorithm together with multi‐objective test case selection optimization. Concretely, in this work, we used four state‐of‐the‐art multi‐objective algorithms and two mono‐objective evolutionary algorithms followed by a multi‐objective test case selection based on Pareto efficiency. The experimental analysis compares these techniques on two different benchmarks. The first one is composed of 800 Java programs created through a program generator. The second benchmark is composed of 13 real programs extracted from the literature. In the direct multi‐objective approach, the results indicate that the oracle cost can be properly optimized; however, the full branch coverage of the system poses a great challenge. Regarding the mono‐objective algorithms, although they need a second phase of test case selection for reducing the oracle cost, they are very effective in maximizing the branch coverage. Copyright © 2011 John Wiley & Sons, Ltd.     

An approach to optimization of fault tolerant architectures using HiP‐HOPS

New processes for the design of dependable systems must address both cost and dependability concerns. They should also maximize the potential for automation to address the problem of increasing technological complexity and the potentially immense design spaces that need to be explored. In this paper we show a design process that integrates system modelling, automated dependability analysis and evolutionary optimization techniques to achieve the optimization of designs with respect to dependability and cost from the early stages. Computerized support is provided for difficult aspects of fault tolerant design, such as decision making on the type and location of fault detection and fault tolerant strategies. The process is supported by HiP‐HOPS, a scalable automated dependability analysis and optimization tool. The process was applied to a Pre‐collision system for vehicles at an early stage of its design. The study shows that HiP‐HOPS can overcome the limitations of earlier work based on Reliability Block Diagrams by enabling dependability analysis and optimization of architectures that may have a network topology and exhibit multiple failure modes. Copyright © 2011 John Wiley & Sons, Ltd.     

Boundary‐based optimization strategies for design of fragment‐type isolation structure in compact MIMO systems

Fragment‐type structures have been used to acquire high isolation in compact multiple‐input and multiple‐output (MIMO) systems. In this paper, two novel optimization strategies, boundary‐based two‐dimensional (2D) median filtering operator and boundary‐based 2D weighted sum filtering operator, are proposed to design fragment‐type isolation structures first when specific boundary conditions are considered in engineering designs. Second, two computer aided optimization techniques are proposed through combining these two operators with MOEA/D‐GO (multi‐objective evolutionary algorithm based on decomposition combined with enhanced genetic operators), respectively. Finally, fragment‐type isolation structures of a compact MIMO PIFAs (planar inverted‐F antennas) system operating at 2.345‐2.36 GHz are designed. Comparison results show that more alternative designs could be found at the expense of searching speed, and both better front‐back‐ratio and wider impedance bandwidth are observed.     

Multi‐Objective Cost‐Detectability in Unfalsified Adaptive Control

Vector‐valued controller cost functions that are solely data‐dependent and reflect multiple objectives of a control system are examined within the framework of unfalsified adaptive control. The notion of Pareto optimality of vector‐valued cost functions and the conditions under which they are cost‐detectable are discussed. A sampled data/discrete‐time Level‐Set controller switching algorithm is investigated which allows for the relaxation of the assumption that the controller cost function be monotonically nondecreasing in time. This opens up the possibility of the use of fading memory cost functions which are nonmonotone. When an active controller is falsified at the current threshold cost level, the Level‐Set switching algorithm replaces it by an effectively unique solution of the weighted Tchebycheff method, thus ensuring the selection of an unfalsified Pareto optimal controller. Theoretical results for convergence and stability of the adaptive system are given. Simulation results validate the use of cost‐detectable multi‐objective cost functions. An example of a cost‐detectable cost function which uses fading memory norm of the fictitious tracking error as a performance measure is shown. This allows for computation of performance of nonactive controllers with respect to a reference model.     

Application of bacteria foraging algorithm for the design optimization of multi‐objective Yagi‐Uda array

The design of antenna array with desirable multiple performance parameters such as directivity, input impedance, beam width, and side‐lobe level using any optimization algorithm is a highly challenging task. Bacteria Foraging Algorithm (BFA), as reported by electrical engineers, is the most robust and efficient algorithm in comparison with other presently available algorithms for global optimization of multi‐objective, multi‐parameter design problems. The objective of this article is to apply this new optimization technique, BFA, in the design of Yagi‐Uda array for multi‐objective design parameters. We optimize length and spacing for 6 and 15 elements array to achieve higher directivity, pertinent input impedance, minimum 3‐dB beam width, and maximum front to back ratio both in the E and H planes of the array. At first, we develop a Method of Moments code in MATLAB environment for the Yagi‐Uda array structure for obtaining the above design parameters and then coupled with the BFA for the evaluation of the optimized design parameters. Detail simulation results are included to confirm the design criteria. © 2010 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2010.     

Solving scalarized multi‐objective network flow problems using an interior point method

In this paper, we present a primal‐dual interior‐point algorithm to solve a class of multi‐objective network flow problems. More precisely, our algorithm is an extension of the single‐objective primal infeasible dual feasible inexact interior point method for multi‐objective linear network flow problems. Our algorithm is contrasted with standard interior point methods and experimental results on bi‐objective instances are reported. The multi‐objective instances are converted into single objective problems with the aid of an achievement function, which is particularly adequate for interactive decision‐making methods.     

Gradient‐free method for distributed multi‐agent optimization via push‐sum algorithms

This paper studies the problem of minimizing the sum of convex functions that all share a common global variable, each function is known by one specific agent in the network. The underlying network topology is modeled as a time‐varying sequence of directed graphs, each of which is endowed with a non‐doubly stochastic matrix. We present a distributed method that employs gradient‐free oracles and push‐sum algorithms for solving this optimization problem. We establish the convergence by showing that the method converges to an approximate solution at the expected rate of , where T is the iteration counter. A numerical example is also given to illustrate the proposed method. Copyright © 2014 John Wiley & Sons, Ltd.     

Pareto‐Set Based Multi‐Objective Dynamic Reactive Power and Voltage Control

A novel approach is proposed in this paper to solve multi‐objective dynamic reactive power and voltage control (Volt/VAR control, VVC). The method is able to attain the Pareto‐optimal solutions, based on the day‐ahead load forecast, for the VVC considering reducing daily power loss, enhancing voltage profile and optimizing dispatch schedules for on‐load tap changer (OLTC) and shunt capacitor switching, which will provide decision maker more options to schedule the dynamic VVC. This approach is simulated in IEEE14 buses system and IEEE30 buses system, and the results are encouraging with respect to performance in dynamic reactive power control. Moreover, the application in an actual distribution system verifies its effectiveness further.     

Multi‐Tracking of First Order Multi‐Agent Networks Via Self‐Triggered Control

A multi‐tracking problem of multi‐agent networks is investigated in this paper where multi‐tracking refers to that the states of multiple agents in each subnetwork asymptotically converge to the same desired trajectory in the presence of information exchanges among subnetworks. The multi‐tracking of first order multi‐agent networks with directed topologies was studied. Self‐triggered protocols were proposed along with triggering functions to solve the stationary multi‐tracking and bounded dynamic multi‐tracking. The self‐triggered scheduling is obtained, and the system does not exhibit Zeno behavior. Numerical examples are provided to illustrate the effectiveness of the obtained criteria.     

参数化白车身结构轻量化多目标优化

利用SFE Concept建立某轿车白车身的参数化模型,采用有限元法对白车身的静态弯曲和扭转刚度、主要低阶模态进行分析,并将仿真结果与试验结果进行对比。将参数化白车身与动力总成、底盘、闭合件连接后,仿真分析整车正面100%碰撞安全性能并验证有限元模型的有效性。提出通过相对灵敏度分析确定白车身非安全件设计变量的方法,采用最优拉丁超立方方法生成样本点,基于径向基神经网络方法拟合近似模型,以白车身非安全件和正碰安全件为轻量化对象,通过第二代非劣排序遗传算法对白车身进行多目标优化设计。结果表明：在白车身静态弯曲刚度降低3.60%、静态扭转刚度降低3.91%、一阶弯曲模态固有频率降低0.09%、一阶扭转模态固有频率上升1.26%、正碰安全性能基本不变的情况下,白车身质量减少24.17 kg,减重7.42%,轻量化效果显著。