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Purpose - The purpose of this paper is to present results concerning resource distribution problems for a class of production systems composed of production operations described by relations with uncertain parameters - for newly considered serial-parallel and parallel-serial structures. Design/methodology/approach - The resource distribution problems are formulated. For the typical mathematical model of a single production operation, the resource distribution algorithms are derived analytically or, when that is impossible, numerical procedures for finding solutions to distribution problems are suggested. Findings - The uncertain variables have been proved as a convenient tool for solving specific optimization problems in production management based on relational knowledge representation with unknown parameters. Originality/value - The paper presents analytical resource distribution and numerical algorithms for determining the optimal resource distribution, which may be applied in knowledge-based management-level decision systems for serial-parallel and parallel-serial structures of production systems. [ABSTRACT FROM AUTHOR]

In model predictive control (MPC), the control input is computed by solving a constrained finite-time optimal control (CFTOC) problem at each sample in the control loop. The main computational effort when solving the CFTOC problem using an active-set (AS) method is often spent on computing the search directions, which in MPC corresponds to solving unconstrained finite-time optimal control (UFTOC) problems. This is commonly performed using Riccati recursions or generic sparsity exploiting algorithms. In this paper, the focus is efficient search direction computations for AS type methods. The system of equations to be solved at each AS iteration is changed only by a low-rank modification of the previous one, and exploiting this structured change is important for the performance of AS-type solvers. In this paper, theory for how to exploit these low-rank changes by modifying the Riccati factorization between AS iterations in a structured way is presented. A numerical evaluation of the proposed algorithm shows that the computation time can be significantly reduced by modifying, instead of re-computing, the Riccati factorization. This speedup can be important for AS-type solvers used for linear, nonlinear, and hybrid MPC. [ABSTRACT FROM PUBLISHER]

This paper considers the problem of a leader that seeks to optimally influence the opinions of agents in a directed network through connecting with a limited number of the agents (“direct followers”), possibly in the presence of a fixed competing leader. The settings involving a single leader and two competing leaders are unified into a general combinatoric optimization problem, for which two heuristic approaches are developed. The first approach is based on a convex relaxation scheme, possibly in combination with the $\ell _1$ -norm regularization technique, and the second is based on a greedy selection strategy. The main technical novelties of this work are in the establishment of supermodularity of the objective function and convexity of its continuous relaxation. The greedy approach is guaranteed to have a lower bound on the approximation ratio sharper than $(1-1/e)$ , while the convex approach can benefit from efficient (customized) numerical solvers to have practically comparable solutions possibly with faster computation times. The two approaches can be combined to provide improved results. In numerical examples, the approximation ratio can be made to reach ${\text{90}\%}$ or higher depending on the number of direct followers. [ABSTRACT FROM AUTHOR]

This paper presents a constructive method for (sub)optimal finite-impulse response (FIR) approximation of infinite-impulse response (IIR) models. The method minimizes the Hankel norm of the approximation error by using an explicit solution to the norm-preserved dilation problem. It has advantages over the existing methods in that it is a unified method for both single-input single-output and multiple-input multiple-output systems which allows direct tradeoff between .the least-squares and Chebyshev error criteria by using a single tuning parameter, and that the approximation algorithm is constructive and only involves algebraic manipulations rather than iteration and convex optimization procedures. The lower and upper bounds on the l² and Chebyshev norms of the approximation error are derived and are related to the tuning parameter. The problem of approximating noncausal IIR models by causal FIR models is also formulated and solved. The effectiveness and properties of the proposed algorithms are demonstrated by examples. [ABSTRACT FROM AUTHOR]

It is well known that one of the basic advantages of evolutionary programming is in deciding the most suitable place for breeding offspring and finding the route toward the global minimum. To reach this goal, the algorithm needs to estimate the coordinates of the global minimum and then steer the new point toward it. In this paper, the estimation of the global minimum is calculated by weighted mean coordination of individuals (WMP), and then a road is mapped between the coordinates of the parents and the WMP. In the proposed method, fuzzy logic is used for deciding on the road and the best coordination to breed offspring. The proposed algorithm is tested on 65 well-known cost functions and is compared with five algorithms inside the EP family. In the next section of the paper, the algorithm is tested on the high-dimensional problem of modeling ozone layer data, which includes almost 26,000 unknown parameters. The results demonstrate the capability of the proposed method in having acceptable speed and accuracy. [ABSTRACT FROM AUTHOR]

Purpose - The aim of the paper is to present the theory and algorithms based on the methods of systems optimal control for a numerical solution of a defined mathematical model of a system as well as that of a mathematical model of game theory. Design/methodology/approach - The paper brings a formulation of the mathematical model of a problem of systems optimal control with distributed parameters in Hilbert space. The mathematical model of the optimal control problem includes equations that also occur in the defined mathematical model of the theory of a two player zero-sum game. Optimization problems of game theory have been defined for the purpose of finding a saddle point of a functional satisfying task constraints ?>0. Findings - In order to find a saddle point of a functional and that one of a functional with a limitation, a designed algorithm of an iterative gradient method is presented. Furthermore, the paper contains a concept of algorithms designing that can be applied to a numerical solution of the defined problem of game theory. These algorithms can be realized on the basis of the methods of systems optimal control. After an adjoint state of the system is defined, a saddle point of a functional will be characterized by equations and inequalities. Originality/value - The contribution of the paper lies in the formulation of the theorems which express the necessary and sufficient conditions of optimality for saddle points of a functional. Furthermore, it has been proved that algorithms of methods of systems optimal control with distributed parameters can be used for the solution of a mathematical model of game theory. The paper contains original results achieved by the authors within scientific projects. [ABSTRACT FROM AUTHOR]

Abstract: In this paper, a suitable model of distributed supply-chains (DSCs) is presented with the aim of providing a tool for DSC decentralised optimisation. To cope with this challenge, in the first part of the paper, a general model for distributed supply-chain including suppliers, processing units, assemblers, and transportation systems is presented with the aim of keeping the framework as general as possible. In the second part of the paper, an optimisation algorithm is also discussed. [Copyright &y& Elsevier]

This paper presents an adjoint-based algorithm for performing automatic parameter identification on differential equation models of biological systems. The algorithm locally solves an optimization problem, in which the cost reflects the deviation between the observed data and the output of the parameterized mathematical model, and the constraints are the governing parameterized equations. The tractability and the speed of convergence (to local minima) of the algorithm are strongly favorable to numerical parameter search algorithms which do not make use of the adjoint. Furthermore, initializing the algorithm with different instantiations of the parameters allows one to effectively search the parameter space. Results of the application of this algorithm to a previously presented mathematical model of planar cell polarity (PCP) signaling in the wings of Drosophila melanogaster are presented, and some new insights into the PCP mechanism that are enabled by the algorithm are described. [ABSTRACT FROM AUTHOR]

Considering the practical application background in peer-to-peer network system, we investigate the single-variable term semi-latticized fuzzy relation geometric programming with max-product operator in this paper. Based on the characteristics of the objective function and feasible domain, we develop a matrix approach to deal with the proposed problem. A step-by-step algorithm is proposed to find an optimal solution without solving all the minimal solutions of the constraint. Two examples are given to illustrate the feasibility and efficiency of the algorithm. [ABSTRACT FROM AUTHOR]

The diversity of solutions is of great importance for multi-objective evolutionary algorithms. In this paper, a new multi-objective particle swarm optimization algorithm based on decomposition (MPSO/D) is proposed. Firstly, the objective space of a multi-objective problem is decomposed into a set of sub-regions based on a set of direction vectors. Then MPSO/D makes each sub-region have a solution to maintain the diversity. Secondly, considering the convergence of solutions, MPSO/D uses the crowding distance to calculate the fitness values of the reserved solutions for selection operator, and uses the neighboring particles of a particle to determine the global best historical position ( gbest ) of the particle. The proposed algorithm has been compared with NSGAII, MOEA/D and NNIA on sixteen test sets. The experimental results illustrate that the proposed algorithm outperforms NSGAII, MOEA/D and NNIA in terms of convergence and diversity. [ABSTRACT FROM AUTHOR]

To avoid traffic accidents, drivers must constantly be aware of nearby vehicles. Unfortunately, nearby vehicles often go unnoticed because of various obstacles such as other vehicles, buildings, or poor weather. In this paper, we study Moving range k -nearest neighbor (MR k NN) queries as a tool for continuously monitoring nearby moving objects. A simple approach to processing MR k NN queries is to have each object periodically broadcast information regarding its movements (i.e., location and velocity at a particular time) to other objects. However, this simple technique cannot be used to process MR k NN queries owing to the limited network bandwidth in mobile peer-to-peer environments. Therefore, we address this bandwidth limitation by proposing a probabilistic algorithm, called MINT, for M ov I ng range k - N N queries with quali T y guarantee over uncertain moving objects. MINT provides users with approximate answers with a quality guarantee, rather than exact answers, with near optimal communication costs. Using a series of simulations, we demonstrate the efficiency and efficacy of MINT in evaluating MR k NN queries with a quality guarantee. [ABSTRACT FROM AUTHOR]

The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function optimization problems. In this paper, we propose an IWD ensemble known as the Master-River, Multiple-Creek IWD (MRMC-IWD) model, which serves as an extension of the modified IWD algorithm. The MRMC-IWD model aims to improve the exploration capability of the modified IWD algorithm. It comprises a master river which cooperates with multiple independent creeks to undertake optimization problems based on the divide-and-conquer strategy. A technique to decompose the original problem into a number of sub-problems is first devised. Each sub-problem is then assigned to a creek, while the overall solution is handled by the master river. To empower the exploitation capability, a hybrid MRMC-IWD model is introduced. It integrates the iterative improvement local search method with the MRMC-IWD model to allow a local search to be conducted, therefore enhancing the quality of solutions provided by the master river. To evaluate the effectiveness of the proposed models, a series of experiments pertaining to two combinatorial problems, i.e., the travelling salesman problem (TSP) and rough set feature subset selection (RSFS), are conducted. The results indicate that the MRMC-IWD model can satisfactorily solve optimization problems using the divide-and-conquer strategy. By incorporating a local search method, the resulting hybrid MRMC-IWD model not only is able to balance exploration and exploitation, but also to enable convergence towards the optimal solutions, by employing a local search method. In all seven selected TSPLIB problems, the hybrid MRMC-IWD model achieves good results, with an average deviation of 0.021% from the best known optimal tour lengths. Compared with other state-of-the-art methods, the hybrid MRMC-IWD model produces the best results (i.e. the shortest and uniform reducts of 20 runs) for all13 selected RSFS problems. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimization method inspired by a paradigm from nature is introduced. Chemical reactions are used as a paradigm to propose an algorithm that can be considered as a general purpose optimization technique. The proposed algorithm is described in detail and then a set of typical complex benchmark functions is used to evaluate the performance of the algorithm. Simulation results show that the proposed optimization algorithm can outperform other methods in a set of benchmark functions. [ABSTRACT FROM AUTHOR]

This paper presents an implementation of an extended simplification algorithm of water distribution network models for the purpose of inclusion in the online optimisation strategy for energy and leakage management in water distribution systems. Whereas the previously proposed reduced model represented accurately the original hydraulic water network characteristics, the energy distribution in the simplified model was not preserved. This could cause a situation where the pump speed required to satisfy specified minimum pressure constraints is different for the reduced model and the original model. This problem has been identified, and an appropriate modification to the simplification algorithm has been introduced. The idea comprises introduction of the energy audit of the water network and the calculation of new minimum service pressure constraints for the simplified model. The approach allows the preservation of both hydraulic and energetic characteristics of the original water network and therefore meets the requirements of the online optimisation strategy. Suitability of the proposed approach is evaluated via a case study. The modern parallel programming implementation allowed water network models consisting of several thousand elements to be reduced within 2 min with an average relative accuracy of less than 2% in terms of tanks flows. [ABSTRACT FROM AUTHOR]

Abstract: In this paper an evolving local model network (LMN) which is especially suited for engine modelling is presented and discussed. The incremental construction of the model tree allows to gradually increase the model complexity while a proper initialisation of new model parameters is easily possible when the LMN is extended. Especially in dynamic system identification the computational speed is an important requirement for online training. Therefore, a new evolving optimisation algorithm for the online training of the LMN is proposed which allows for a recursive computation of the model parameters. while the local interpretability of the consequent parameters is conserved. The decision when to grow the tree is based on an effective statistical criterion. The proposed concepts are validated by means of an illustrative example and by real dynamic measurement data from a state-of-the-art 4-cylinder EURO5 diesel engine. [Copyright &y& Elsevier]

Abstract: We present a hybrid global–local optimization algorithm for parameter estimation of radial basis function (RBF) networks and the RBF-type autoregressive models without exogenous inputs (RBF-AR) or with exogenous inputs (RBF-ARX). The RBF-AR (X) models are quasi-linear time-varying AR (X) models with Gaussian RBF network-style coefficients, which have been used to effectively model the nonlinear behavior of various complex systems. However, the identification of these models is a difficult optimization problem because of the large number of local minima. A hybrid approach is proposed in this paper to achieve better optimization results for these RBF-type models. The applied hybrid search strategy (EA-SNPOM) is developed by combining an evolutionary algorithm (EA) with a gradient-based algorithm known as the structured nonlinear parameter optimization method (SNPOM). This strategy makes use of the robustness of the EA to cover an entire global search space and the efficiency of the gradient search to converge to a local optimum. Several examples of time series modeling and system identification are presented. The simulation results indicate that the performance of the proposed hybrid approach is better than the performance obtained from using each method (EA or SNPOM) alone. Furthermore, the RBF-AR (X) models estimated by the EA-SNPOM achieve much better modeling accuracy relative to other neural networks or fuzzy models in the simulations. [Copyright &y& Elsevier]

This paper presents a simulation--optimization-based method for identification of contamination source characteristics in a water distribution system using filtered data from threshold-based binary water quality signals. The effects of quality and quantity of the data on the accuracy of the source identification methodology are investigated. This study also addresses the issue of non-uniqueness in contaminant source identification under various data availability conditions. To establish the robustness and applicability of the methodology, numerous scenarios are investigated for a wide range of contamination incidents associated with two different networks. Results indicate that, even though use of lower resolution sensors lead to more non-unique solutions, the true source location is always included among these solutions. [ABSTRACT FROM AUTHOR]

Abstract: Transparency, accuracy, compactness and reliability all appear to be vital (even though somewhat contradictory) requirements when it comes down to linguistic fuzzy modeling. This paper presents a methodology for simultaneous optimization of these criteria by chaining previously published various algorithms – a heuristic fully automated identification algorithm that is able to extract sufficiently accurate, yet reliable and transparent models from data and two algorithms for subsequent simplification of the model that are able to reduce the number of output parameters as well as the number of fuzzy rules with only a marginal negative effect to the accuracy of the model. [Copyright &y& Elsevier]

Abstract: Many optimization problems in real-world applications contain both explicit (quantitative) and implicit (qualitative) indices that usually contain uncertain information. How to effectively incorporate uncertain information in evolutionary algorithms is one of the most important topics in information science. In this paper, we study optimization problems with both interval parameters in explicit indices and interval uncertainties in implicit indices. To incorporate uncertainty in evolutionary algorithms, we construct a mathematical uncertain model of the optimization problem considering the uncertainties of interval objectives; and then we transform the model into a precise one by employing the method of interval analysis; finally, we develop an effective and novel evolutionary optimization algorithm to solve the converted problem by combining traditional genetic algorithms and interactive genetic algorithms. The proposed algorithm consists of clustering of a large population according to the distribution of the individuals and estimation of the implicit indices of an individual based on the similarity among individuals. In our experiments, we apply the proposed algorithm to an interior layout problem, a typical optimization problem with both interval parameters in the explicit index and interval uncertainty in the implicit index. Our experimental results confirm the feasibility and efficiency of the proposed algorithm. [Copyright &y& Elsevier]

Abstract: This paper proposes a novel and unconventional Memetic Computing approach for solving continuous optimization problems characterized by memory limitations. The proposed algorithm, unlike employing an explorative evolutionary framework and a set of local search algorithms, employs multiple exploitative search within the main framework and performs a multiple step global search by means of a randomized perturbation of the virtual population corresponding to a periodical randomization of the search for the exploitative operators. The proposed Memetic Computing approach is based on a populationless (compact) evolutionary framework which, instead of processing a population of solutions, handles its statistical model. This evolutionary framework is based on a Differential Evolution which cooperatively employs two exploitative search operators: the first is based on a standard Differential Evolution mutation and exponential crossover, and the second is the trigonometric mutation. These two search operators have an exploitative action on the algorithmic framework and thus contribute to the rapid convergence of the virtual population towards promising candidate solutions. The action of these search operators is counterbalanced by a periodical stochastic perturbation of the virtual population, which has the role of “disturbing” the excessively exploitative action of the framework and thus inhibits its premature convergence. The proposed algorithm, namely Disturbed Exploitation compact Differential Evolution, is a simple and memory-wise cheap structure that makes use of the Memetic Computing paradigm in order to solve complex optimization problems. The proposed approach has been tested on a set of various test problems and compared with state-of-the-art compact algorithms and with some modern population based meta-heuristics. Numerical results show that Disturbed Exploitation compact Differential Evolution significantly outperforms all the other compact algorithms present in literature and reaches a competitive performance with respect to modern population algorithms, including some memetic approaches and complex modern Differential Evolution based algorithms. In order to show the potential of the proposed approach in real-world applications, Disturbed Exploitation compact Differential Evolution has been implemented for performing the control of a space robot by simulating the implementation within the robot micro-controller. Numerical results show the superiority of the proposed algorithm with respect to other modern compact algorithms present in literature. [Copyright &y& Elsevier]

Abstract: Increasing attention is being paid to solve constrained optimization problems (COP) frequently encountered in real-world applications. In this paper, an improved vector particle swarm optimization (IVPSO) algorithm is proposed to solve COPs. The constraint-handling technique is based on the simple constraint-preserving method. Velocity and position of each particle, as well as the corresponding changes, are all expressed as vectors in order to present the optimization procedure in a more intuitively comprehensible manner. The NVPSO algorithm , which uses one-dimensional search approaches to find a new feasible position on the flying trajectory of the particle when it escapes from the feasible region, has been proposed to solve COP. Experimental results showed that searching only on the flying trajectory for a feasible position influenced the diversity of the swarm and thus reduced the global search capability of the NVPSO algorithm. In order to avoid neglecting any worthy position in the feasible region and improve the optimization efficiency, a multi-dimensional search algorithm is proposed to search within a local region for a new feasible position. The local region is composed of all dimensions of the escaped particle’s parent and the current positions. Obviously, the flying trajectory of the particle is also included in this local region. The new position is not only present in the feasible region but also has a better fitness value in this local region. The performance of IVPSO is tested on 13 well-known benchmark functions. Experimental results prove that the proposed IVPSO algorithm is simple, competitive and stable. [ABSTRACT FROM AUTHOR]

Abstract: Establishing blockmodels for one- and two-mode binary network matrices has typically been accomplished using multiple restarts of heuristic algorithms that minimize functions of inconsistency with an ideal block structure. Although these algorithms likely yield exceptional performance, they are not assured to provide blockmodels that optimize the functional indices. In this paper, we present integer programming models that, for a prespecified image matrix, can produce guaranteed optimal solutions for matrices of nontrivial size. Accordingly, analysts performing a confirmatory analysis of a prespecified blockmodel structure can apply our models directly to obtain an optimal solution. In exploratory cases where a blockmodel structure is not prespecified, we recommend a two-stage procedure, where a heuristic method is first used to identify an image matrix and the integer program is subsequently formulated and solved to identify the optimal solution for that image matrix. Although best suited for ideal block structures associated with structural equivalence, the integer programming models have the flexibility to accommodate functional indices pertaining to regular equivalence. Computational results are reported for a variety of one- and two-mode matrices from the blockmodeling literature. [Copyright &y& Elsevier]

Abstract: This paper presents a new fuzzy chance-constrained programming model to find the solution for multiproject and multi-item investment combination in investment combination problems. The proposed 0–1 integer programming model has three objectives with fuzzy constraints, and NSGA-II is applied to solve the optimization model with a small modification of the constraint-handling rule. A simulation experiment illustrating the application of the proposed model is presented and Pareto-optimal solutions are obtained through a modified NSGA-II algorithm. A comparison among NSGA-II, PSO, and DE shows that modified NSGA-II has some advantages over PSO and DE. [Copyright &y& Elsevier]