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Abstract: The aim of this note is to show that the optimization algorithm proposed in may not lead to the optimal solution in some cases. In fact, the optimization problem remains open if we do not apply the branch-and-bound method to solve it or compute all the objective values of minimal solutions of the feasible domain. [Copyright &y& Elsevier]

This paper proposes a novel optimization algorithm for grouping problems, the Grouping Coral Reefs Optimization algorithm, and describes its application to a Mobile Network Deployment Problem (MNDP) under four optimization criteria. These criteria include economical cost and coverage, and also electromagnetic pollution control and capacity constraints imposed at the base stations controllers, which are novel in this study. The Coral Reefs Optimization algorithm (CRO) is a recently-proposed bio-inspired approach for optimization, based on the simulation of the processes that occur in coral reefs, including reproduction, fight for space or depredation. This paper presents a grouping version of the CRO, which has not previously evaluated before. Grouping meta-heuristics are characterized by variable-length encoding solutions, and have been successfully applied to a number of different optimization and assignment problems. The GCRO proposed is a novel contribution to the intelligent systems field, which is able to improve results obtained by two alternative grouping algorithms such as grouping genetic algorithms and grouping Harmony Search. The performance of the proposed GCRO and the algorithms for comparison has been tested with real data in a case study of a MNDP in Alcalá de Henares, Madrid, Spain. [ABSTRACT FROM AUTHOR]

Our paper introduces a new methodology to solve group decision-making problems under fuzzy and dynamic environment. The methodology takes group members’ linguistically defined pair wise preferences of alternatives in different time intervals and aggregates them across the intervals to obtain each member's net preference levels. Each member's net preference levels are again aggregated across the members to obtain the group's preference. Our paper attaches higher importance to the members whose involvement in the decision process is more recent than the members who opined their views in the past. The fuzzy aggregation operator, IOWA (Induced Ordered Weighted Average) is used to aggregate their views in accordance to their importance in the group. The Ranked_List algorithm, introduced in our paper, inputs the aggregated views of the members in pair wise form and produces the set of sequences of ranked list of alternatives representing the group's consensus view as output. The Ranked_List algorithm is validated and analyzed through a series of synthetic data sets and its results are compared with a movie selection case study. The methodology is illustrated with a numerical example. [ABSTRACT FROM AUTHOR]

Generalizing both mixed-integer linear optimization and convex optimization, mixed-integer convex optimization possesses broad modeling power but has seen relatively few advances in general-purpose solvers in recent years. In this paper, we intend to provide a broadly accessible introduction to our recent work in developing algorithms and software for this problem class. Our approach is based on constructing polyhedral outer approximations of the convex constraints, resulting in a global solution by solving a finite number of mixed-integer linear and continuous convex subproblems. The key advance we present is to strengthen the polyhedral approximations by constructing them in a higher-dimensional space. In order to automate this extended formulation we rely on the algebraic modeling technique of disciplined convex programming (DCP), and for generality and ease of implementation we use conic representations of the convex constraints. Although our framework requires a manual translation of existing models into DCP form, after performing this transformation on the MINLPLIB2 benchmark library we were able to solve a number of unsolved instances and on many other instances achieve superior performance compared with state-of-the-art solvers like Bonmin, SCIP, and Artelys Knitro. [ABSTRACT FROM AUTHOR]

This paper presents a novel metaheuristic algorithm called queuing search (QS), which is inspired from human activities in queuing. Some common phenomena are considered in QS: (1) customers actively follow the queue that provides fast service; (2) each customer service is mainly affected by the staff or customer itself; and (3) a customer can be influenced by others during the service when the queue order is not strictly maintained. The performance of QS is tested on 30 bound-constrained benchmark functions scalable with 30 and 100 dimensions from CEC 2014, 5 standard and 4 challenging constrained engineering optimization problems. Meanwhile, comparisons are performed among the results of QS and some state-of-the-art or well-known metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Many evolutionary multi-objective optimization (EMO) methodologies have been proposed and performed very well on finding a representative set of Pareto-optimal solutions, but this advantage will be weakened with the increasing number of objectives. And in real applications, what decision makers (DMs) want is a unique solution or a set of solutions rather than the overall Pareto-optimal front. It is a difficult task to solve many-objective problems by using preference information provided by decision maker (DM) during optimization process. In this paper, a new angle-based preference selection mechanism is proposed, which replaces the traditional crowding distance with the aid of preference information provided by the DMs. Particularly, we combine it with a multi-objective immune algorithm with non-dominated neighbor-based selection. The proposed method has been extensively compared with other recently proposed preference-based EMO approaches over DTLZ1, DTLZ2, and DTLZ3 test problems with 4-100 objectives. The results of the experiment indicate that the proposed algorithm can achieve competitive and better results. [ABSTRACT FROM AUTHOR]

In this paper, an integrated coal gasification combined cycle system for the production of hydrogen and electricity is optimized in terms of energy and exergy efficiencies, and the amount and cost of the produced hydrogen and electricity. The integrated system is optimized by focusing on the conversion process of coal to syngas. A novel optimization process is developed which integrates an artificial neural network with a genetic algorithm. The gasification system is modeled and simulated with Aspen Plus for large ranges of operating conditions, where the artificial neural network method is used to represent the simulation results mathematically. The mathematical model is then optimized using a genetic algorithm method. The optimization demonstrates that the lower is the grade of coal of the three considered coals, the less expensive is the hydrogen and electricity that can be produced by the considered integrated gasification combined cycle (IGCC) system. [ABSTRACT FROM AUTHOR]

In this paper, we examine the mathematical structure of chemical reaction networks with the goals of identifying reaction invariant states and determining their physical significance. A combined species-reaction graph/convex analysis approach is developed to find semi-positive invariant states associated with a reaction network. Application of this graphical/algebraic reaction network analysis approach to four different chemical processes reveals that reaction invariants can represent conserved quantities other than elemental balances. [ABSTRACT FROM AUTHOR]

Artificial gas-lift (AGL) technique is commonly used to enhance oil production when the reservoir pressure in wells is not enough to sustain acceptable oil flow rate. However, the gas-lift wells are prone to instability, characterized by regular oscillations of pressure and flow. This phenomenon is known as casing-heading instability. It results in production loss and negative impact on downstream equipment, and has been a challenging problem to both industry and academia. In this paper, a novel concept of optimal tracking control is proposed for stabilization and operating mode transition in gas-lift wells when casing-heading phenomenon occurs. The stability of artificial gas-lift process is ensured by manipulating both gas lift choke and oil production choke, where the openings of both choke valves can vary from fully closed to fully open. Through the simulation of the open-loop system, a stability map of AGL process is produced. Then a trajectory optimization algorithm is developed based on this stability map, which is synthesized with a tracking controller to achieve trajectory optimization control. Also, a nonlinear state observer is designed to ensure estimation of unmeasurable variables. Through simulation studies, the effectiveness of proposed trajectory optimization control is demonstrated. [ABSTRACT FROM AUTHOR]

Visual recognition of crowd dynamics has had a huge impact on several applications including surveillance, situation awareness, homeland security and intelligent environments. However, the state-of-the-art in crowd analysis has become diverse due to factors such as: (a) the underlying definition of a crowd, (b) the constituent elements of the crowd processing model, (c) its application, hence (d) the dataset and (e) the evaluation criteria available for benchmarking. Although such diversity is healthy, the techniques for crowd modelling thus developed have failed to establish credibility therefore becoming unreliable and of questionable validity across different research disciplines. This paper aims to give an account of such issues by deducing key statistical evidence from the existing literature and providing recommendations towards focusing on the general aspects of techniques rather than any specific algorithm. The advances and trends in crowd analysis are drawn in the context of crowd modelling studies published in leading journals and conferences over the past 7 years. Finally, this paper shall also provide a qualitative and quantitative comparison of some existing methods using various publicly available crowd datasets to substantiate some of the theoretical claims. [ABSTRACT FROM AUTHOR]

In this paper, a three-term conjugate gradient algorithm is developed for solving large-scale unconstrained optimization problems. The search direction at each iteration of the algorithm is determined by rectifying the steepest descent direction with the difference between the current iterative points and that between the gradients. It is proved that such a direction satisfies the approximate secant condition as well as the conjugacy condition. The strategies of acceleration and restart are incorporated into designing the algorithm to improve its numerical performance. Global convergence of the proposed algorithm is established under two mild assumptions. By implementing the algorithm to solve 75 benchmark test problems available in the literature, the obtained results indicate that the algorithm developed in this paper outperforms the existent similar state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we proposed an implementation of stochastic perturbation of reduced gradient and bisection (SPRGB) method for optimizing a non-convex differentiable function subject to linear equality constraints and non-negativity bounds on the variables. In particular, at each iteration, we compute a search direction by reduced gradient, and optimal line search by bisection algorithm along this direction yields a decrease in the objective value. SPRGB method is desired to establish the global convergence of the algorithm. An implementation and tests of SPRGB algorithm are given, and some numerical results of large-scale problems are presented, which show the efficient of this approach. [ABSTRACT FROM AUTHOR]

This paper studies the problem of color consistency correction for sequential images with diverse color characteristics. Existing algorithms try to adjust all images to minimize color differences among images under a unified energy framework, however, the results are prone to presenting a consistent but unnatural appearance when the color difference between images is large and diverse. In our approach, this problem is addressed effectively by providing a guided initial solution for the global consistency optimization, which avoids converging to a meaningless integrated solution. First of all, to obtain the reliable intensity correspondences in overlapping regions between image pairs, we creatively propose the histogram extreme point matching algorithm which is robust to image geometrical misalignment to some extents. In the absence of the extra reference information, the guided initial solution is learned from the major tone of the original images by searching some image subset as the reference, whose color characteristics will be transferred to the others via the paths of graph analysis. Thus, the final results via global adjustment will take on a consistent color similar to the appearance of the reference image subset. Several groups of convincing experiments on both the synthetic dataset and the challenging real ones sufficiently demonstrate that the proposed approach can achieve as good or even better results compared with the state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The adaptive operator selection (AOS) and the adaptive parameter control are widely used to enhance the search power in many multiobjective evolutionary algorithms. This paper proposes a novel adaptive selection strategy with bandits for the multiobjective evolutionary algorithm based on decomposition (MOEA/D), named latest stored information based adaptive selection (LSIAS). An improved upper confidence bound (UCB) method is adopted in the strategy, in which the operator usage rate and abandonment of extreme fitness improvement are introduced to improve the performance of UCB. The strategy uses a sliding window to store recent valuable information about operators, such as factors, probabilities, and efficiency. Four common used DE operators are chosen with the AOS, and two kinds of assist information on operator are selected to improve the operators search power. The operator information is updated with the help of LSIAS and the resulting algorithmic combination is called MOEA/D-LSIAS. Compared to some well-known MOEA/D variants, the LSIAS demonstrates the superior robustness and fast convergence for various multiobjective optimization problems. The comparative experiments also demonstrate improved search power of operators with different assist information on different problems. [ABSTRACT FROM AUTHOR]

The nonmonotone globalization technique is useful in difficult nonlinear problems, because of the fact that it may help escaping from steep sided valleys and may improve both the possibility of finding the global optimum and the rate of convergence. This paper discusses the nonmonotonicity degree of nonmonotone line searches for the unconstrained optimization. Specifically, we analyze some popular nonmonotone line search methods and explore, from a computational point of view, the relations between the efficiency of a nonmonotone line search and its nonmonotonicity degree. We attempt to answer this question how to control the degree of the nonmonotonicity of line search rules in order to reach a more efficient algorithm. Hence in an attempt to control the nonmonotonicity degree, two adaptive nonmonotone rules based on the morphology of the objective function are proposed. The global convergence and the convergence rate of the proposed methods are analysed under mild assumptions. Numerical experiments are made on a set of unconstrained optimization test problems of the CUTEr (Gould et al. in ACM Trans Math Softw 29:373-394, 2003) collection. The performance data are first analysed through the performance profile of Dolan and Moré (Math Program 91:201-213, 2002). In the second kind of analyse, the performance data are analysed in terms of increasing dimension of the test problems. [ABSTRACT FROM AUTHOR]

In this paper, a Shuffled Frog Leaping Algorithm (SFLA) is proposed to address the Profit-Based Unit Commitment (PBUC) problem under deregulation. The PBUC problem is one of the major tasks for a power producer and is a highly complex, multi-constrained, nonlinear optimization problem. Some of the recently developed algorithms like GA, PSO, BBO, ACO, etc. are available to solve this complex problem, but so far there has been no such ideal technique which can completely handle the computational time and the large dimensionality of the problem. In this scenario, an attempt is made to solve this problem using SFLA. The problem is formulated as a biobjective optimization problem with profit maximization of generation companies as one objective and emission level limitation of generating units as second objective. The proposed algorithm is tested on IEEE 10 unit 10 hours load demand as input data for simulation using MATLAB. The results are compared with GA and PSO. From the results, it is observed that SFLA effectively handles the dimension of the problem and achieves maximum profit and minimum production cost with less computational time. [ABSTRACT FROM AUTHOR]

Scheduling of jobs and resources on a shop floor is an ever green optimization problem. Job shop scheduling problem (JSSP) is an allocation of 'n' jobs on 'm' machines so as to complete processing of all jobs in a minimum possible time. The JSSP has been addressed by various direct, indirect methods, programs, and algorithms in the last 40 year of literature. This paper presents a new paradigm of invasive weed optimization which mimics the process of weed colonization and distribution to solve JSSPs. The algorithm had shown encouraging and promising outputs on standard benchmarking problems. [ABSTRACT FROM AUTHOR]

This paper comprehensively reviews the spiral dynamics optimization (SDO) algorithm and investigates its characteristics. SDO algorithm is one of the most straightforward physics-based optimization algorithms and is successfully applied in various broad fields. This paper describes the recent advances of the SDO algorithm, including its adaptive, improved, and hybrid approaches. The growth of the SDO algorithm and its application in various areas, theoretical analysis, and comparison with its preceding and other algorithms are also described in detail. A detailed description of different spiral paths, their characteristics, and the application of these spiral approaches in developing and improving other optimization algorithms are comprehensively presented. The review concludes the current works on the SDO algorithm, highlighting its shortcomings and suggesting possible future research perspectives. [ABSTRACT FROM AUTHOR]

In transport planning, one should allow passengers to travel through the complicated transportation scheme with efficient use of different modes of transport. In this paper, we propose the use of a cockroach swarm optimization algorithm for determining paths with the shortest travel time. In our approach, this algorithm has been modified to work with the time-expanded model. Therefore, we present how the algorithm has to be adapted to this model, including correctly creating solutions and defining steps and movement in the search space. By introducing the proposed modifications, we are able to solve journey planning. The results have shown that the performance of our approach, in terms of converging to the best solutions, is satisfactory. Moreover, we have compared our results with Dijkstra's algorithm and a particle swarm optimization algorithm. [ABSTRACT FROM AUTHOR]

In this paper, a parametric simplex algorithm for solving linear vector optimization problems (LVOPs) is presented. This algorithm can be seen as a variant of the multi-objective simplex (the Evans-Steuer) algorithm (Math Program 5(1):54-72, 1973). Different from it, the proposed algorithm works in the parameter space and does not aim to find the set of all efficient solutions. Instead, it finds a solution in the sense of Löhne (Vector optimization with infimum and supremum. Springer, Berlin, 2011), that is, it finds a subset of efficient solutions that allows to generate the whole efficient frontier. In that sense, it can also be seen as a generalization of the parametric self-dual simplex algorithm, which originally is designed for solving single objective linear optimization problems, and is modified to solve two objective bounded LVOPs with the positive orthant as the ordering cone in Ruszczyński and Vanderbei (Econometrica 71(4):1287-1297, 2003). The algorithm proposed here works for any dimension, any solid pointed polyhedral ordering cone C and for bounded as well as unbounded problems. Numerical results are provided to compare the proposed algorithm with an objective space based LVOP algorithm [Benson's algorithm in Hamel et al. (J Global Optim 59(4):811-836, 2014)], that also provides a solution in the sense of Löhne (2011), and with the Evans-Steuer algorithm (1973). The results show that for non-degenerate problems the proposed algorithm outperforms Benson's algorithm and is on par with the Evans-Steuer algorithm. For highly degenerate problems Benson's algorithm (Hamel et al. 2014) outperforms the simplex-type algorithms; however, the parametric simplex algorithm is for these problems computationally much more efficient than the Evans-Steuer algorithm. [ABSTRACT FROM AUTHOR]

Most complex networks contain community structures. Detecting these community structures is important for understanding and controlling the networks. Most community detection methods use network topology and edge density to identify optimal communities; however, these methods have a high computational complexity and are sensitive to network forms and types. To address these problems, in this paper, we propose an algorithm that uses an interaction optimization process to detect community structures in complex networks. This algorithm efficiently searches the candidates of optimal communities by optimizing the interactions of the members within each community based on the concept of greedy optimization. During this process, each candidate is evaluated using an interaction-based community model. This model quickly and accurately measures the difference between the quantity and quality of intra- and inter-community interactions. We test our algorithm on several benchmark networks with known community structures that include diverse communities detected by other methods. Additionally, after applying our algorithm to several real-world complex networks, we compare our algorithm with other methods. We find that the structure quality and coverage results achieved by our algorithm surpass those of the other methods. [ABSTRACT FROM AUTHOR]

The scheduling in grids is known to be a NP-hard problem. The distributed deployment of nodes, their heterogeneity and their fluctuations in terms of workload and availability make the design of an effective scheduling algorithm a very complex issue. The scientific literature has proposed several heuristics able to tackle this kind of optimization problem using techniques and strategies inspired by nature. The algorithms belonging to ant colony optimization (ACO) paradigm represent an example of these techniques: each one of these algorithms uses strategies inspired by the self-organization ability of real ants for building effective grid schedulers. In this paper, the authors propose an on line, non-clairvoyant, distributed scheduling solution for multi-broker grid based on the alienated ant algorithm (AAA), a new ACO inspired technique exploiting a 'non natural' behavior of ants and an inverse interpretation of pheromone trails. The paper introduces the proposed algorithm, explains the differences with other classical ACO approaches, and compares AAA with two different algorithms. The results of simulations show that the AAA guarantees good performance in terms of makespan, average queue waiting time and load balancing capability. [ABSTRACT FROM AUTHOR]

Second-order necessary optimality conditions play an important role in optimization theory. This is explained by the fact that most numerical optimization algorithms reduce to finding stationary points satisfying first-order necessary optimality conditions. As a rule, optimization problems, especially the high dimensional ones, have a lot of stationary points so one has to use second-order necessary optimality conditions to exclude nonoptimal points. These conditions are closely related to second-order constraint qualifications, which guarantee the validity of second-order necessary optimality conditions. In this paper, strong and weak second-order necessary optimality conditions are considered and their validity proved under so-called critical regularity condition at local minimizers. [ABSTRACT FROM PUBLISHER]

Abstract: In this paper, we present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. The Taguchi method is widely used in the industry for optimizing the product and the process conditions. By adopting the Taguchi method into the tracing mode process of the PCSO method, we propose the EPCSO method with better accuracy and less computational time. In this paper, five test functions are used to evaluate the accuracy of the proposed EPCSO method. The experimental results show that the proposed EPCSO method gets higher accuracies than the existing PSO-based methods and requires less computational time than the PCSO method. We also apply the proposed method to solve the aircraft schedule recovery problem. The experimental results show that the proposed EPCSO method can provide the optimum recovered aircraft schedule in a very short time. The proposed EPCSO method gets the same recovery schedule having the same total delay time, the same delayed flight numbers and the same number of long delay flights as the . The optimal solutions can be found by the proposed EPCSO method in a very short time. [Copyright &y& Elsevier]

In this paper, we study the link between a Chance-Constrained optimization Problem (CCP) and its sample counterpart (SP). SP has a finite number, say N, of sampled constraints. Further, some of these sampled constraints, say k, are discarded, and the final solution is indicated by $x^{\ast}_{N,k}$. Extending previous results on the feasibility of sample convex optimization programs, we establish the feasibility of $x^{\ast}_{N,k}$ for the initial CCP problem. Constraints removal allows one to improve the cost function at the price of a decreased feasibility. The cost improvement can be inspected directly from the optimization result, while the theory here developed permits to keep control on the other side of the coin, the feasibility of the obtained solution. In this way, trading feasibility for performance is put on solid mathematical grounds in this paper. The feasibility result here obtained applies to a vast class of chance-constrained optimization problems, and has the distinctive feature that it holds true irrespective of the algorithm used to discard k constraints in the SP problem. For constraints discarding, one can thus, e.g., resort to one of the many methods introduced in the literature to solve chance-constrained problems with discrete distribution, or even use a greedy algorithm, which is computationally very low-demanding, and the feasibility result remains intact. We further prove that, if constraints in the SP problem are optimally removed-i.e., one deletes those constraints leading to the largest possible cost improvement-, then a precise optimality link to the original chance-constrained problem CCP in addition holds. [ABSTRACT FROM AUTHOR]

In this paper, we consider approximation algorithms for optimizing a generic multi-variate homogeneous polynomial function, subject to homogeneous quadratic constraints. Such optimization models have wide applications, e.g., in signal processing, magnetic resonance imaging (MRI), data training, approximation theory, and portfolio selection. Since polynomial functions are non-convex, the problems under consideration are all NP-hard in general. In this paper we shall focus on polynomial-time approximation algorithms. In particular, we first study optimization of a multi-linear tensor function over the Cartesian product of spheres. We shall propose approximation algorithms for such problem and derive worst-case performance ratios, which are shown to be dependent only on the dimensions of the model. The methods are then extended to optimize a generic multi-variate homogeneous polynomial function with spherical constraint. Likewise, approximation algorithms are proposed with provable approximation performance ratios. Furthermore, the constraint set is relaxed to be an intersection of co-centered ellipsoids; namely, we consider maximization of a homogeneous polynomial over the intersection of ellipsoids centered at the origin, and propose polynomial-time approximation algorithms with provable worst-case performance ratios. Numerical results are reported, illustrating the effectiveness of the approximation algorithms studied. [ABSTRACT FROM AUTHOR]

This paper presents a dynamic predictive-optimization framework of a nonlinear temporal process. Data-mining (DM) and evolutionary strategy algorithms are integrated in the framework for solving the optimization model. DM algorithms learn dynamic equations from the process data. An evolutionary strategy algorithm is then applied to solve the optimization problem guided by the knowledge extracted by the DM algorithm. The concept presented in this paper is illustrated with the data from a power plant, where the goal is to maximize the boiler efficiency and minimize the limestone consumption. This multiobjective optimization problem can be either transformed into a single-objective optimization problem through preference aggregation approaches or into a Pareto-optimal optimization problem. The computational results have shown the effectiveness of the proposed optimization framework. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, the penalty function method is used to study duality in nonconvex mathematical programming problems. In particular, we prove the zero duality gap between optimization problems involving invex functions with respect to the same function η and their Lagrangian dual problems. The results proved in the paper are illustrated by suitable examples of nonconvex optimization problems. [Copyright &y& Elsevier]

Abstract  The study and research of evolutionary algorithms (EAs) is getting great attention in recent years. Although EAs have earned extensive acceptance through numerous successful applications in many fields, the problem of finding the best combination of evolutionary parameters especially for population size that need the manual settings by the user is still unresolved. In this paper, our system is focusing on differential evolution (DE) and its control parameters. To overcome the problem, two new systems were carried out for the self-adaptive population size to test two different methodologies (absolute encoding and relative encoding) in DE and compared their performances against the original DE. Fifty runs are conducted for every 20 well-known benchmark problems to test on every proposed algorithm in this paper to achieve the function optimization without explicit parameter tuning in DE. The empirical testing results showed that DE with self-adaptive population size using relative encoding performed well in terms of the average performance as well as stability compared to absolute encoding version as well as the original DE. [ABSTRACT FROM AUTHOR]

This paper tackles a power minimization problem of interleave-division multiple-access (IDMA) systems over a fading multiple-access channel. The problem is minimizing the total power received by the receiver while keeping the bit error rates (BERs) of all users below a predefined value. The original formulation of the problem has highly nonlinear and implicitly defined functions, which render most existing optimization methods incapable. A new formulation is proposed in this paper, whose solution can effectively be obtained by a branch-and-bound (B&B) technique. An algorithm is devised based on B&B, and its effectiveness is also demonstrated by numerical experiments of systems with a moderate numbers of users. [ABSTRACT FROM AUTHOR]

We present a new computational approach to the problem of placing n identical nonoverlapping disks in the unit square in such a way that their radii are maximized. The problem has been studied in a large number of papers, from both a theoretical and a computational point of view. In this paper, we conjecture that the problem possesses a so-called funneling landscape, a feature that is commonly found in molecular conformation problems. Based on this conjecture, we develop a stochastic search algorithm that displays excellent numerical performance. Thanks to this algorithm, we could improve over previously known putative optima in the range n ≤ 130 in as many as 32 instances, the smallest of which is n=53. [ABSTRACT FROM AUTHOR]

Abstract  This paper introduces a novel version of the particle swarm optimisation (PSO) algorithm which we call self-organising swarm SOSwarm. SOSwarm can be used for unsupervised learning. In the algorithm, input vectors are projected into a lower-dimensional map space producing a visual representation of the input data in a manner similar to a self-organising map (SOM). In SOSwarm, particles react to input data during the learning process by modifying their velocities using an adaptation of the PSO velocity update function. SOSwarm is successfully applied to ten benchmark problems drawn from the UCI Machine Learning repository. The paper also demonstrates how the canonical SOM can be explored within the PSO paradigm. Illustrating this linkage between the heretofore distinct literatures of SOM and PSO opens up several new avenues of research for the development of novel self-organising algorithms. [ABSTRACT FROM AUTHOR]

This paper considers localization of a source or a sensor from distance measurements. We argue that linear algorithms proposed for this purpose are susceptible to poor noise performance. Instead given a set of sensors/anchors of known positions and measured distances of the source/sensor to be localized from them we propose a potentially nonconvex weighted cost function whose global minimum estimates the location of the source/sensor one seeks. The contribution of this paper is to provide nontrivial ellipsoidal and polytopic regions surrounding these sensors/anchors of known positions, such that if the object to be localized is in this region, localization occurs by globally exponentially convergent gradient descent in the noise free case. Exponential convergence in the noise free case represents practical convergence as it ensures graceful performance degradation in the presence of noise. These results guide the deployment of sensors/anchors so that small subsets can be made responsible for practical localization in geographical areas determined by our approach. [ABSTRACT FROM AUTHOR]

Abstract: An algorithm for polygonal approximation based on dominant point (DP) deletion is presented in this paper. The algorithm selects an initial set of DPs and starts eliminating them one by one depending upon the error associated with each DP. The associated error value is based on global measure. A local optimization of few neighboring points is performed after each deletion. Although the algorithm does not guarantee an optimal solution, the combination of local and global optimization is expected to produce optimal results. The algorithm is extensively tested on various shapes with varying number of DPs and error threshold. In general, optimal results were observed for about 96% of the times. A good comparative study is also presented in this paper [Copyright &y& Elsevier]

The majorize-minimize (MM) optimization technique has received considerable attention in signal and image processing applications, as well as in statistics literature. At each iteration of an MM algorithm, one constructs a tangent majorant function that majorizes the given cost function and is equal to it at the current iterate. The next iterate is obtained by minimizing this tangent majorant function, resulting in a sequence of iterates that reduces the cost function monotonically. A well-known special case of MM methods are expectation-maximization algorithms. In this paper, we expand on previous analyses of MM, due to Fessler and Hero, that allowed the tangent majorants to be constructed in iteration-dependent ways. Also, this paper overcomes an error in one of those earlier analyses. There are three main aspects in which our analysis builds upon previous work. First, our treatment relaxes many assumptions related to the structure of the cost function, feasible set, and tangent majorants. For example, the cost function can be nonconvex and the feasible set for the problem can be any convex set. Second, we propose convergence conditions, based on upper curvature bounds, that can be easier to verify than more standard continuity conditions. Furthermore, these conditions allow for considerable design freedom in the iteration-dependent behavior of the algorithm. Finally, we give an original characterization of the local region of convergence of MM algorithms based on connected (e.g., convex) tangent majorants. For such algorithms, cost function minimizers will locally attract the iterates over larger neighborhoods than typically is guaranteed with other methods. This expanded treatment widens the scope of the MM algorithm designs that can be considered for signal and image processing applications, allows us to verify the convergent behavior of previously published algorithms, and gives a fuller understanding overall of how these algorithms behave. [ABSTRACT FROM AUTHOR]

Infinitesimal perturbation analysis (IPA) provides formulas for random gradients (derivatives) of performance measures with respect to parameters of interest, computed from sample paths of stochastic systems. In practice, IPA derivatives may be computed either from simulation runs or from empirical field data (when the formulas are nonparametric). Nonparametric IPA derivatives in fluid-flow queues have been recently derived for the loss volume and time average of buffer occupancy, with respect to buffer size, and arrival-rate or service-rate parameters. Additionally, these IPA derivatives have been shown to be unbiased in the sense that their expectation and differentiation operators commute, while their traditional discrete counterparts have long been known to be generally biased. Recent work has further shown how to map the computation of IPA derivatives from a fluid-flow queue to a compatible discrete counterpart without an appreciable loss of accuracy in performance measures. Thus, this work holds the promise of potential applications of IPA derivatives to gradient-based optimization of objective functions involving performance metrics parameterized by settable parameters in a queueing network context. This paper is an empirical study of IPA derivatives of individual queues within queueing systems which model telecommunications networks and some of their protocols. As a testbed, we used HNS (Hybrid Network Simulator) — a hybrid Java simulator of queueing networks with traffic streams subject to several telecommunications protocols. More specifically, the hybrid feature of HNS admits models with mixtures of discrete (packet) flows and continuous (fluid) flows, and collects detailed statistics and IPA derivatives for all flow types. The paper outlines the mapping of IPA derivatives from the fluid domain to the packet domain as implemented in HNS, and studies the accuracy of IPA derivatives in compatible fluid and packet queueing models, as well as the stabilization of their values in time. Our experimental results lend empirical support to the contention that IPA derivatives can be accurately computed from discrete versions by adopting a fluid-flow view. Furthermore, the long-run values of various IPA derivatives are empirically shown to stabilize quite fast. Finally, the results provide the basis and motivation for IPA applications to the optimization of telecommunications network design and to potential new open-loop protocols that take advantage of IPA information. [Copyright &y& Elsevier]

This paper focuses on a class of discrete-time interconnected pendulumlike systems. Sufficient conditions for the global convergence of systems with both structured and unstructured uncertainties in its linear part are established in terms of linear matrix inequalities (LMIs). In the traditional decentralized control of large scale systems, the effects of interconnections were seldom studied. In this paper, a square matrix specifying the interconnecting structure is introduced in order to discuss the effects of nonlinear interconnections. It is shown that the global convergence of the whole interconnected system can be achieved by designing an appropriate interconnection matrix. In order to solve the nonlinear matrix inequalities (NMIs) arising in the synthesis problem, a global optimization algorithm is presented which can be used to handle a class of NMI problems. An illustrative example is given to demonstrate the applicability and validity of the main results and the presented algorithm. [ABSTRACT FROM AUTHOR]

Abstract: “Transshipment” is a very popular and important issue in the present international trade container transportation market. In order to reduce the international trade container transportation operation cost, it is very important for shipping companies to choose the best transshipment container port. The aim of this paper is to present a new Fuzzy Multiple Criteria Decision Making Method (FMCDM) for solving the transshipment container port selection problem under fuzzy environment. In this paper we present first the canonical representation of multiplication operation on three fuzzy numbers, and then this canonical representation is applied to the selection of transshipment container port. Based on the canonical representation, the decision maker of shipping company can determine quickly the ranking order of all candidate transshipment container ports and select easily the best one. [Copyright &y& Elsevier]

This paper proposes several globally convergent geometric optimization algorithms on Riemannian manifolds, which extend some existing geometric optimization techniques. Since any set of smooth constraints in the Euclidean space Rn (corresponding to constrained optimization) and the Rn space itself (corresponding to unconstrained optimization) are both special Riemannian manifolds, and since these algorithms are developed on general Riemannian manifolds, the techniques discussed in this paper provide a uniform framework for constrained and unconstrained optimization problems. Unlike some earlier works, the new algorithms have less restrictions in both convergence results and in practice. For example, global minimization in the one-dimensional search is not required. All the algorithms addressed in this paper are globally convergent. For some special Riemannian manifold other than Rn, the newalgorithms are very efficient. Convergence rates are obtained. Applications are discussed. [ABSTRACT FROM AUTHOR]

Abstract: The job-shop scheduling problem (JSSP) is a branch of production scheduling, and it is well known that this problem is NP-hard. Many different approaches have been applied to JSSP and a rich harvest has been obtained. However, some JSSP, even with moderate size, cannot be solved to guarantee optimality. The standard particle optimization algorithm generally is used to solve continuous optimization problems, and is used rarely to solve discrete problems such as JSSP. This paper presents a similar PSO algorithm to solve JSSP. At the same time, some new valid algorithm operators are proposed in this paper, and through simulation we find out the effectiveness of them. Three representative (Taillard) instances were made by computational experiments, through comparing the SPSO algorithm with standard GA, and we obtained that the SPSOA is more clearly efficacious than standard GA for JSSP to minimize makespan. [Copyright &y& Elsevier]

This paper addresses the problem of minimization of a nonsmooth function under general nonsmooth constraints when no derivatives of the objective or constraint functions are available. We introduce the mesh adaptive direct search (MADS) class of algorithms which extends the generalized pattern search (GPS) class by allowing local exploration, called polling, in an asymptotically dense set of directions in the space of optimization variables. This means that under certain hypotheses, including a weak constraint qualification due to Rockafellar, MADS can treat constraints by the extreme barrier approach of setting the objective to infinity for infeasible points and treating the problem as unconstrained. The main GPS convergence result is to identify limit points x0302;, where the Clarke generalized derivatives are nonnegative in a finite set of directions, called refining directions. Although in the unconstrained case, nonnegative combinations of these directions span the whole space, the fact that there can only be finitely many GPS refining directions limits rigorous justification of the barrier approach to finitely many linear constraints for GPS. The main result of this paper is that the general MADS framework is flexible enough to allow the generation of an asymptotically dense set of refining directions along which the Clarke derivatives are nonnegative. We propose an instance of MADS for which the refining directions are dense in the hypertangent cone at x0302; with probability 1 whenever the iterates associated with the refining directions converge to a single x0302;. The instance of MADS is compared to versions of GPS on some test problems. We also illustrate the limitation of our results with examples. [ABSTRACT FROM AUTHOR]

Abstract: This paper proposes an improvement of an efficient multiobjective optimization algorithm, Non-dominated Sorting Genetic Algorithm II, NSGA-II, that has been here applied to solve the problem of optimal capacitors placement in distribution systems. The studied improvement involves the Crowded Comparison Operator and modifies it in order to handle several constraints. The problem of optimal location and sizing of capacitor banks for losses reduction and voltage profile flattening in medium voltage (MV) automated distribution systems is a difficult combinatorial constrained optimization problem which is deeply studied in literature. In this paper, the efficiency of the proposed Crowded Comparison Operator, CCO1, is compared to the efficiency of another Crowded Comparison Operator, CCO2, whose definition derives from the constraint-domination principle proposed by Deb et al. The two operators are tested on difficult test problems as well as on the optimal capacitors placement problem. [Copyright &y& Elsevier]

Abstract: In this paper, a new definition of the filled function is given, it is different from the primary definition which was given by Ge in paper [R.P. Ge, A filled function method for finding a global minimzer of a function of several variables, Math. Program. 46 (1990) 191–204]. Based on the definition, a new filled function is proposed, and it has better properties. An algorithm for unconstrained global optimization is developed from the new filled function. The implementation of the algorithm on several test problems is reported with satisfactory numerical results. [Copyright &y& Elsevier]

In this paper we use the penalty approach to study three constrained minimization problems. A penalty function is said to have the exact penalty property [J.-B. Hiriart-Urruty and C. Lemarechal, Convex Analysis and Minimization Algorithms, 2 vols., Springer-Verlag, Berlin, 1993] if there exists a penalty coefficient for which a solution of an unconstrained penalized problem is a solution of the corresponding constrained problem. In this paper we establish a very simple sufficient condition for the exact penalty property. [ABSTRACT FROM AUTHOR]