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In this paper, denoising of smooth ( H10-regular) images is considered. The purpose of the paper is basically twofold. First, to compare the denoising methods based on L1- and L2-fitting. Second, to analyze and realize an active-set method for solving the non-smooth optimization problem arising from the former approach. More precisely, we formulate the algorithm, proof its convergence, and give an efficient numerical realization. Several numerical experiments are presented, where the convergence of the proposed active-set algorithm is studied and the denoising properties of the methods based on L1- and L2-fitting are compared. Also a heuristic method for determining the regularization parameter is presented and tested. [ABSTRACT FROM AUTHOR]

This paper deals with optimization problems on rotagraphs and fasciagraphs. These graphs are repetitive structures that generalize grids and toroidal grids, respectively. We develop a theoretical framework and get linear-time and constant-time generic algorithms for wide classes of combinatorial problems which are defined in a purely combinatorial way. These classes of problems include in particular many classical optimization problems that are NP-complete in general. Our results unify in a single framework many results of the literature, in particular on the topic of domination–and its variants–in grids. [ABSTRACT FROM AUTHOR]

This paper proposes a new formulation for the multi-objective optimal power flow (MOOPF) problem for meshed power networks considering distributed generation. An efficacious multi-objective fuzzy linear programming optimization (MFLP) algorithm is proposed to solve the aforementioned problem with and without considering the distributed generation (DG) effect. A variant combination of objectives is considered for simultaneous optimization, including power loss, voltage stability, and shunt capacitors MVAR reserve. Fuzzy membership functions for these objectives are designed with extreme targets, whereas the inequality constraints are treated as hard constraints. The multi-objective fuzzy optimal power flow (OPF) formulation was converted into a crisp OPF in a successive linear programming (SLP) framework and solved using an efficient interior point method (IPM). To test the efficacy of the proposed approach, simulations are performed on the IEEE 30-busand IEEE 118-bus test systems. The MFLP optimization is solved for several optimization cases. The obtained results are compared with those presented in the literature. A unique solution with a high satisfaction for the assigned targets is gained. Results demonstrate the effectiveness of the proposed MFLP technique in terms of solution optimality and rapid convergence. Moreover, the results indicate that using the optimal DG location with the MFLP algorithm provides the solution with the highest quality. [ABSTRACT FROM AUTHOR]

In this paper, existence of critical point and weak efficient point of vector optimization problem is studied. A sequence of points in n-dimension is generated using positive definite matrices like Quasi-Newton method. It is proved that accumulation points of this sequence are critical points or weak efficient points under different conditions. An algorithm is provided in this context. This method is free from any kind of priori chosen weighting factors or any other form of a priori ranking or ordering information for objective functions. Also, this method does not depend upon initial point. The algorithm is verified in numerical examples. [ABSTRACT FROM AUTHOR]

Two new PRP conjugate Algorithms are proposed in this paper based on two modified PRP conjugate gradient methods: the first algorithm is proposed for solving unconstrained optimization problems, and the second algorithm is proposed for solving nonlinear equations. The first method contains two aspects of information: function value and gradient value. The two methods both possess some good properties, as follows: 1)βk ≥ 0 2) the search direction has the trust region property without the use of any line search method 3) the search direction has sufficient descent property without the use of any line search method. Under some suitable conditions, we establish the global convergence of the two algorithms. We conduct numerical experiments to evaluate our algorithms. The numerical results indicate that the first algorithm is effective and competitive for solving unconstrained optimization problems and that the second algorithm is effective for solving large-scale nonlinear equations. [ABSTRACT FROM AUTHOR]

This paper proposes and analyses a mathematical model for the problem of distribution of a finite number of irradiation sources during radiotherapy in continuous environments to maximize the minimal cumulative effects. A new algorithm based on nondifferentiable optimization techniques has been developed to solve this problem. [ABSTRACT FROM AUTHOR]

In this paper we study the convex problem of optimizing the sum of a smooth function and a compactly supported nonsmooth term with a specific separable form. We analyze the block version of the generalized conditional gradient method when the blocks are chosen in a cyclic order. A global sublinear rate of convergence is established for two different stepsize strategies commonly used in this class of methods. Numerical comparisons of the proposed method to both the classical conditional gradient algorithm and its random block version demonstrate the effectiveness of the cyclic block update rule. [ABSTRACT FROM AUTHOR]

Derivative-free optimization is an area of long history which has so many applications in different fields. It has lately received considerable attention within the engineering community. This paper describes a random derivative-free algorithm for solving unconstrained or bound constrained continuously differentiable non-linear problems. This method is a combination of particle swarm and directional direct search algorithms. The key difference in direct search methods is in the way of generating positive bases. At first glance, a simple way of generating positive bases has been introduced for solving continuously differentiable problems. Then, it has been shown that using the particle swarm algorithm with a direct search algorithm can solve non-linear optimization problems efficiently. Some standard examples have been presented to demonstrate the ability and effectiveness of this approach. [ABSTRACT FROM AUTHOR]

Software requirements selection is a problem which consists of choosing the set of new requirements which will be included in the next release of a software package. This NP-hard problem is an important issue involving several contradictory objectives which have to be tackled by software companies when developing new releases of software packages. Software projects have to stick to a budget, but they also have to satisfy the highest number of customer requirements. Furthermore, when managing real instances of the problem, the requirements tackled suffer interactions and other restrictions which make the problem even harder. In this paper, a novel multi-objective teaching learning based optimization (TLBO) algorithm has been successfully applied to several instances of the problem. For doing this, the software requirements selection problem has been formulated as a multiobjective optimization problem with two objectives: the total software development cost and the overall customer׳s satisfaction. In addition, three interaction constraints have been also managed. In this context, the original TLBO algorithm has been adapted to solve real instances of the problem generated from data provided by experts. Numerical experiments with case studies on software requirements selection have been carried out in order to prove the effectiveness of the multiobjective proposal. In fact, the obtained results show that the developed algorithm performs better than other relevant algorithms previously published in the literature. [ABSTRACT FROM AUTHOR]

In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problems. These algorithms have been compared with the traditional ones and have demonstrated to be superior in many complex problems. This paper attempts to describe the algorithms based on nature, which are used in optimizing fuzzy clustering in real-world applications. We briefly describe the optimization methods, the most cited ones, nature-inspired algorithms that have been published in recent years, authors, networks and relationship of the works, etc. We believe the paper can serve as a basis for analysis of the new area of nature and bio-inspired optimization of fuzzy clustering. [ABSTRACT FROM AUTHOR]

Coordinate descent algorithms solve optimization problems by successively performing approximate minimization along coordinate directions or coordinate hyperplanes. They have been used in applications for many years, and their popularity continues to grow because of their usefulness in data analysis, machine learning, and other areas of current interest. This paper describes the fundamentals of the coordinate descent approach, together with variants and extensions and their convergence properties, mostly with reference to convex objectives. We pay particular attention to a certain problem structure that arises frequently in machine learning applications, showing that efficient implementations of accelerated coordinate descent algorithms are possible for problems of this type. We also present some parallel variants and discuss their convergence properties under several models of parallel execution. [ABSTRACT FROM AUTHOR]

Highly dynamic networks rarely offer end-to-end connectivity at a given time. Yet, connectivity in these networks can be established over time and space, based on temporal analogues of multi-hop paths (also called journeys). Attempting to optimize the selection of the journeys in these networks naturally leads to the study of three cases: shortest (minimum hop), fastest (minimum duration), and foremost (earliest arrival) journeys. Efficient centralized algorithms exists to compute all cases, when the full knowledge of the network evolution is given. In this paper, we study the distributed counterparts of these problems, i.e. shortest, fastest, and foremost broadcast with termination detection (TDB), with minimal knowledge on the topology. We show that the feasibility of each of these problems requires distinct features on the evolution, through identifying three classes of dynamic graphs wherein the problems become gradually feasible: graphs in which the re-appearance of edges is recurrent (class ), bounded-recurrent (), or periodic (), together with specific knowledge that are respectively n (the number of nodes), Δ (a bound on the recurrence time), and p (the period). In these classes it is not required that all pairs of nodes get in contact - only that the overall footprint of the graph is connected over time. Our results, together with the strict inclusion between , , and , implies a feasibility order among the three variants of the problem, i.e. TDB[ foremost] requires weaker assumptions on the topology dynamics than TDB[ shortest], which itself requires less than TDB[ fastest]. Reversely, these differences in feasibility imply that the computational powers of , , and also form a strict hierarchy. [ABSTRACT FROM AUTHOR]

In this paper, we present a new regularized quasi-Newton algorithm for unconstrained optimization. In this algorithm, an adaptive quadratic term is employed to regularize the quasi-Newton model. At each iteration, the trial step is obtained by solving an unconstrained quadratic subproblem. The global convergence and superlinear convergence of the new algorithm are established under reasonable assumptions. The numerical results show that new algorithm is effective. [ABSTRACT FROM AUTHOR]

Second-order cone optimization (denoted by SOCO) is a class of convex optimization problems and it contains the linear optimization problem, convex quadratic optimization problem and quadratically constrained convex quadratic optimization problem as special cases. In this paper, we propose a new smoothing Newton method for solving the SOCO based on a non-symmetrically perturbed smoothing Fischer–Burmeister function. At each iteration, a system of linear equations is solved only approximately by using the inexact Newton method. It is shown that any accumulation point of the iteration sequence generated by the proposed algorithm is a solution of the SOCO. Furthermore, we prove that the generated sequence is bounded and hence it has at least one accumulation point. Under the assumption of nonsingularity, we establish the local quadratic convergence of the proposed algorithm without strict complementarity condition. Numerical experiments indicate that our method is effective. [ABSTRACT FROM AUTHOR]

In this paper, we study the finitely many constraints of fuzzy relation inequalities problem and optimize the linear objective function on this region which is defined with fuzzy max-Lukasiewicz operator. In fact Lukasiewicz t-norm is one of the four basic t-norms. A new simplification technique is given to accelerate the resolution of the problem by removing the components having no effect on the solution process. Also, an algorithm and one numerical example are offered to abbreviate and illustrate the steps of the problem resolution process. [ABSTRACT FROM AUTHOR]

In order to model route planning problem for emergency logistics management taking both route timeliness and safety into account, a multiobjective mathematical model is proposed based on the theories of bounded rationality. The route safety is modeled as the product of safety through arcs included in the path. For solving this model, we convert the multiobjective optimization problem into its equivalent deterministic form. We take uncertainty of the weight coefficient for each objective function in actual multiobjective optimization into account. Finally, we develop an easy-to-implement heuristic in order to gain an efficient and feasible solution and its corresponding appropriate vector of weight coefficients quickly. Simulation results show the effectiveness and feasibility of the models and algorithms presented in this paper. [ABSTRACT FROM AUTHOR]

Under study is a special class of mixed-integer bi-level programming problem (BLPP) which arises in several areas such as engineering, transportation and control systems. The main characteristic of BLPP is that the outer optimization (upper level) problem is constrained by an inner optimization (lower level) problem. In this paper, a Lagrangean relaxation method is proposed to obtain an acceptable feasible solution by solving a sequence of one-level mixed-integer problems. The computational results on some examples were taken from literatures and randomly generated problems indicate that the method is able to solve the BLPP efficiently. [ABSTRACT FROM AUTHOR]

As non-rigid 3D shape plays increasingly important roles in practical applications, this paper addresses its retrieval problem by considering three aspects: shape representation, retrieval optimization, and shape filtering. (1) For shape representation, two kinds of features are considered. We first propose a new integration kernel based local descriptor, and then an efficient voting scheme is designed for shape representation. Besides, we also study the commute times as shape distributions, which grasp the spatial shape information globally. Both of them capture shape information from different viewpoints based on the same embedding basis. (2) We then study the typical problem of retrieval optimization. Prior works show poor stability under different similarity windows. To deal with this deficiency, we propose to model the problem as a distance mapping on a graph in spectral manifold space. (3) Usually, for each retrieval input, a list is returned and there may be lots of irrelevant results. We develop an algorithm to filter them out by combining multiple kernels. Finally, three public datasets are employed for performance evaluation and the results show that the studied techniques have contributed a lot in promoting the recognition rate of non-rigid 3D shapes. [ABSTRACT FROM AUTHOR]

In this paper a successful topology optimization of a centrifugally loaded aero-engine part is presented. For the topology optimization, which is a nonlinear problem due to several contact regions, a self-developed topology optimization algorithm in combination with a commercial FE-solver is used. The goal of the optimization was the reduction of stresses in the structure. [ABSTRACT FROM AUTHOR]

The conventional compliance minimization of load-carrying structures does not directly deal with displacements that are of practical importance. In this paper, a global displacement control is realized through topology optimization with a global constraint that sets a displacement limit on the whole structure or certain sub-domains. A volume minimization problem is solved by an extended evolutionary topology optimization approach. The local displacement sensitivities are derived following a power-law penalization material model. The global control of displacement is realized through multiple local displacement constraints on dynamically located critical nodes. Algorithms are proposed to secure the stability and convergence of the optimization process. Through numerical examples and by comparing with conventional stiffness designs, it is demonstrated that the proposed approach is capable of effectively finding optimal solutions which satisfy the global displacement control. Such solutions are of particular importance for structural designs whose deformed shapes must comply with functioning requirements such as aerodynamic performances. [ABSTRACT FROM AUTHOR]

The component pick-and-place sequence is one of the key factors to affect the working efficiency of the surface mounting machine in the printed circuit board assembly. In this paper, an improved Shuffled Frog-leaping Algorithm was presented by improving the basic Shuffled Frog-leaping Algorithm (SFLA) with the strategy of letting all frogs taking part in memetic evolution and adding the self-variation behavior to the frog. The objective function of component pick-and-place sequence of the gantry multi-head component surface mounting machine was established. Parameters selection is critical for SFLA. In this study, Three-way ANOVA was used in parameters analyzing of the new improved SFLA. The parameters like memeplex numbers m, the frogs' number P and local evolution numbers iPart were found having notable effects on the mounting time (time spent for components picking and placing), but the interactions among these parameters were not obvious. Multiple comparison procedures were adopted to determine the best parameter settings. In order to test the performance of the new algorithm, several experiments were carried out to compare the performance of improved SFLA with the basic SFLA and the genetic algorithm (GA) in solving the component pick-and-place sequence optimization problems. The experiment results indicate that improved SFLA can solve the optimization problem efficiently and outperforms SFLA and GA in terms of convergence accuracy, although more CPU time is undeniably needed. [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 investigate the effectiveness of the selection method in the solution construction phase of the IWD algorithm. Instead of the fitness proportionate selection method in the original IWD algorithm, two ranking-based selection methods, namely linear ranking and exponential ranking, are proposed. Both ranking-based selection methods aim to solve the identified limitations of the fitness proportionate selection method as well as to enable the IWD algorithm to escape from local optima and ensure its search diversity. To evaluate the usefulness of the proposed ranking-based selection methods, a series of experiments pertaining to three combinatorial optimization problems, i.e., rough set feature subset selection, multiple knapsack and travelling salesman problems, is conducted. The results demonstrate that the exponential ranking selection method is able to preserve the search diversity, therefore improving the performance of the IWD algorithm. [ABSTRACT FROM AUTHOR]

Very recently, tropical counterparts of fractional linear programs have been studied. Some algorithms were proposed for solving them, with techniques ranging from bisection methods to homeomorphisms to formal power series. In this paper, some algorithms are also proposed. They mainly rely in the ability of finding the greatest and smallest solutions of tropical equations, a subject that was discussed in a previous work of the authors [13] . [ABSTRACT FROM AUTHOR]

In order to overcome the drawbacks of the K-means (KM) for clustering problems such as excessively depending on the initial guess values and easily getting into local optimum, a clustering algorithm of invasive weed optimization (IWO) and KM based on the cloud model has been proposed in the paper. The so-called cloud model IWO (CMIWO) is adopted to direct the search of KM algorithm to ensure that the population has a definite evolution direction in the iterative process, thus improving the performance of CMIWO K-means (CMIWOKM) algorithm in terms of convergence speed, computing precision and algorithm robustness. The experimental results show that the proposed algorithm has such advantages as higher accuracy, faster constringency, and stronger stability. [ABSTRACT FROM AUTHOR]

In this paper, a novel protection of coordinating optimization algorithm is proposed. Maximizing the penetration's dispersed generation and at the same time minimizing the fuse's operating time are the targets. A novel optimization technique, the Imperialistic Competition Algorithm (ICA), is applied to solve the problem. The results of simulations confirm that the proposed method leads to lower operating times of protective devices and higher possible DG penetration, compared with the traditional coordination techniques. [ABSTRACT FROM AUTHOR]

Invasive Weed Optimization (IWO) algorithm is a simple but powerful algorithm which is capable of solving general multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. Recently IWO algorithm is being used in several engineering design owing to its superior performance in comparison with many other existing algorithms. This paper presents a Chaotic IWO (CIWO) algorithms based on chaos, and investigates its performance for optimal settings of Optimal Power Flow (OPF) control variables of OPF problem with non-smooth and non-convex generator fuel cost curves (non-smooth and non-convex OPF). The performance of CIWO algorithms are studied and evaluated on the standard IEEE 30-bus test system with different objective functions. The experimental results suggest that IWO algorithm holds immense promise to appear as an efficient and powerful algorithm for optimization in the power system. [ABSTRACT FROM AUTHOR]

Nature-inspired metaheuristic algorithms remain a strong trend in optimization. Human-inspired optimization algorithms should be more intuitive and relatable. This paper proposes a novel optimization algorithm inspired by a human search party. We hypothesize the behavioral model of a search party searching for a treasure. Motivated by the search party's behavior, we abstract the "Divide, Conquer, Assemble" (DCA) approach. The DCA approach allows us to parallelize the traditional gradient descent algorithm in a strikingly simple manner. Essentially, multiple gradient descent instances with different learning rates are run parallelly, periodically sharing information. We call it the search party gradient descent (SPGD) algorithm. Experiments performed on a diverse set of classical benchmark functions show that our algorithm is good at optimizing. We believe our algorithm's apparent lack of complexity will equip researchers to solve problems efficiently. We compare the proposed algorithm with SciPy's optimize library and it is found to be competent with it. [ABSTRACT FROM AUTHOR]

• The relationship between apparent soil resistivity and soil parameters. • A high-efficiency and accurate PSO-DE algorithm for soil parameters estimation was proposed. • The initialization method of PSO-DE and other heuristic algorithms for improving the calculation accuracy was proposed. The grounding grid is buried in horizontal multilayer soils, so accurate parameters (resistivity , thickness, the number of soil layers) estimation of the soil is the basis for accurately calculating the grounding parameters and design of grounding grids. At present, the parameters estimation is usually solved by non-heuristic algorithms and heuristic algorithms. However, existing non-heuristic algorithms depend too much on the initial value and conventional heuristic algorithms could deviate from the correct solution too far, which would easily fall into local optimal solution, causing calculation errors. To solve the above problem, this paper first studies the relationship between the apparent soil resistivity and soil parameters based on the Wenner's method. Then, an innovative PSO-DE (Particle Swarm Optimization-Differential Evolution) optimization algorithm and its initialization method are proposed. The results of PSO-DE, conventional heuristic intelligent optimization algorithms, CDEGS, and literatures are compared, which verifies the superiority and accuracy of the PSO-DE. Therefore, the contribution of this work can lay a solid theoretical foundation for grounding parameters calculation. [ABSTRACT FROM AUTHOR]

Students generally attempt to improve themselves with different manners in their spare time. Inspired by this fact, this paper presents a multi-objective elitist feedback teaching–learning-based optimization (MEFTO) algorithm for multi-objective optimization problems. To promote the exploration capacity and convergence of the basic teaching–learning-based optimization (TLBO), a feedback phase that simulates the spare-time learning phenomenon is introduced at the end of the learner phase. Students should compare their score with the average class score to select an appropriate means for further improvement. Poorly performing students can learn from the teacher directly for rapid improvement, whereas high-performing students prefer to motivate themselves for reinforcement learning. Non-dominated sorting is incorporated to permit this heuristic to solve problems with several objectives. Crowding distance calculation is adopted to maintain the diversity of the obtained solution set in a single run. Elitism strategy is also employed to provide a great improvement for the algorithm. The performance of the MEFTO algorithm is compared with seven other well-known algorithms by using a set of unconstrained benchmark test problems and three constrained engineering optimization problems. The qualitative and quantitative results indicate that the proposed algorithm can provide considerably competitive results and outperforms the other algorithms. • A novel multi-objective evolutionary algorithm (MEFTO) is developed. • The developed algorithm is evaluated on a series of benchmark functions. • The performance of the MEFTO is demonstrated using various metrics. • The developed algorithm is applied to solve three constrained engineering problem. [ABSTRACT FROM AUTHOR]

This paper improves the accuracy of a mine robot's positioning and mapping for rapid rescue. Specifically, we improved the FastSLAM algorithm inspired by the lion swarm optimization method. Through the division of labor between different individuals in the lion swarm optimization algorithm, the optimized particle set distribution after importance sampling in the FastSLAM algorithm is realized. The particles are distributed in a high likelihood area, thereby solving the problem of particle weight degradation. Meanwhile, the diversity of particles is increased since the foraging methods between individuals in the lion swarm algorithm are different so that improving the accuracy of the robot's positioning and mapping. The experimental results confirmed the improvement of the algorithm and the accuracy of the robot. [ABSTRACT FROM AUTHOR]

Optimal capacitor placement problem is a significant and precious strategy which can be supposed as an always appropriate solution to reduce the operating costs in the power systems. Nevertheless, as the result of nonlinear and discrete characteristics of this problem, traditional optimization methods can not solve it effectively. Therefore, this paper proposes a novel optimization framework based on shuffled frog leaping algorithm (SFLA) to solve the optimal capacitor placement and sizing problem properly. In this regard, a new modification approach based on the mutation and crossover operators is suggested to improve the local search ability of this algorithm. The effectiveness and satisfying performance of the proposed method is assessed on two IEEE test systems. Comparative results sow the superiority of the proposed optimization method than the other popular methods in the area. [ABSTRACT FROM AUTHOR]

The stagnation phenomena regularly happen in solving complex optimization problems by a class of computational intelligence algorithms which lack theoretical guidance for their parameters setting. The search characteristics of these algorithms are analyzed in this paper, based on which an adaptive computational intelligence optimization algorithm called cloud drops algorithm is proposed by adopting a cloud model to express the randomness and fuzziness of its search process. The cloud drops algorithm is characterized by representing, mining and recreating the uncertain knowledge about its search process for the optimal solution. None of search parameters are predefined in the proposed algorithm and, whatever the initial solution set is, the whole system can adaptively approach to the global optimum. Based on the theory of stochastic processes, the almost sure convergence of the proposed algorithm is proved under certain conditions by introducing a martingale approach into traditional Markov Chain analysis. Two benchmark problems are tested with the proposed algorithm and the other two existing algorithms as a comparison. The results show that the proposed algorithm has faster convergence speed, better self-adaptability, and stronger ability to deal with stagnation phenomena effectively. [ABSTRACT FROM AUTHOR]

This paper considers the problem of clustering a partially observed unweighted graph--i.e., one where for some node pairs we know there is an edge between them, for some others we know there is no edge, and for the remaining we do not know whether or not there is an edge. We want to organize the nodes into disjoint clusters so that there is relatively dense (observed) connectivity within clusters, and sparse across clusters. We take a novel yet natural approach to this problem, by focusing on finding the clustering that minimizes the number of "disagreements"--i.e., the sum of the number of (observed) missing edges within clusters, and (observed) present edges across clusters. Our algorithm uses convex optimization; its basis is a reduction of disagreement minimization to the problem of recovering an (unknown) low-rank matrix and an (unknown) sparse matrix from their partially observed sum. We evaluate the performance of our algorithm on the classical Planted Partition/Stochastic Block Model. Our main theorem provides sufficient conditions for the success of our algorithm as a function of the minimum cluster size, edge density and observation probability; in particular, the results characterize the tradeoff between the observation probability and the edge density gap. When there are a constant number of clusters of equal size, our results are optimal up to logarithmic factors. [ABSTRACT FROM AUTHOR]

Most algorithms for solving linear program require a phase-1 procedure to find a feasible solution. Recently, a dual–primal algorithm for linear optimization has been proposed by Li [Dual–primal algorithm for linear optimization, Optimiz. Methods Softw. 28 (2013), pp. 327–338]. In the process of implementing the dual–primal algorithms, we found an interesting phenomenon that the phase-1 algorithm developed in [Li (2013)] always terminates in one iteration. This fact does not come by chance. A rigorous proof is given in this paper. [ABSTRACT FROM AUTHOR]

This paper proposed two kinds of measurements based on close degree between the cloud models had the same nature concepts. Algorithms were simple, especially still maintained the accuracy in few cloud drops. The experimental results show that the proposed algorithm has obvious optimization accuracy and consumption, meanwhile appeares good feasibility and validity in classification problems based on cloud model. [ABSTRACT FROM AUTHOR]

Highlights: [•] This paper presents TLBO and OTLBO to solve combined heat and power dispatch problem. [•] The proposed methods are implemented on three standard test systems. [•] The effectiveness of the proposed methods are compared with other reported algorithms. [•] Robustness of the proposed methods are checked through statistical analysis. [Copyright &y& Elsevier]

Abstract: The normalized least mean squares (NLMS) and recursive least squares (RLS) algorithms are widely used for adaptive filtering. Interestingly, the NLMS algorithm has been shown to be strictly optimal in the sense of filtering, whereas the forgetting factor RLS algorithm has not been clearly related to a solution to the filtering problem. This paper describes a method for further optimizing the solutions to the ordinary filtering problem over an assumed system model set and a predetermined norm weight set. The extended filtering problem offers a framework for constructing a unified view of adaptive algorithms for finite impulse response (FIR) filters. The framework enables a discussion of the relationships among the NLMS algorithm, the forgetting factor RLS algorithm, and the filter over the common parameter space, and facilitates the development of new fast adaptive algorithms that outperform the existing algorithms, such as the NLMS and the fast RLS algorithms. The validity of the discussion based on the framework is verified using numerical examples. [Copyright &y& Elsevier]

Abstract: In this paper, a novel fruit fly optimization algorithm (nFOA) is proposed to solve the semiconductor final testing scheduling problem (SFTSP). First, a new encoding scheme is presented to represent solutions reasonably, and a new decoding scheme is presented to map solutions to feasible schedules. Second, it uses multiple fruit fly groups during the evolution process to enhance the parallel search ability of the FOA. According to the characteristics of the SFTSP, a smell-based search operator and a vision-based search operator are well designed for the groups to stress exploitation. Third, to simulate the information communication behavior among fruit flies, a cooperative search process is developed to stress exploration. The cooperative search process includes a modified improved precedence operation crossover (IPOX) and a modified multipoint preservative crossover (MPX) based on two popular structures of the flexible job shop scheduling. Moreover, the influence of the parameter setting is investigated by using Taguchi method of design-of-experiment (DOE), and suitable values are determined for key parameters. Finally, computational tests results with some benchmark instances and the comparisons to some existing algorithms are provided, which demonstrate the effectiveness and the efficiency of the nFOA in solving the SFTSP. [Copyright &y& Elsevier]

This work is motivated by two two-sided optimization problems that arise in atomic chemistry when one is looking for a minimal set of localized orbitals with prescribed occupation numbers, respectively, a prescribed total number of electrons. We first propose an optimal analytic solution of the first problem, and then show that an optimal solution of the second problem can be found by solving a convex quadratic programming problem with box constraints and p unknowns. We prove that the latter problem is solved by the active-set method in at most 2p iterations. These investigations reveal that the optimal solutions of both problems are projections that are in C = {Y ∈ Rn×p : YTY = Ip, YT NY = Δ} for N symmetric and Δ diagonal and that these solutions are generally nonunique. The question of how one can then select a particular solution out of the set C leads to the main problem of this paper: the optimization of an arbitrary smooth function whose minimizer describes the solution of interest over C. To solve this problem, we first find that a slight modification of C is a Riemannian manifold for which geometric objects can be derived that are required to allow optimization over this manifold. Using these geometric tools we propose an augmented Lagrangian-based algorithm that guarantees global convergence to a stationary point of our main problem. The latter is shown by investigating when the LICQ is satisfied. Finally we compare this algorithm numerically with a similar algorithm that, however, does not apply these geometric tools and that is, to our knowledge, not guaranteed to converge. Our results show that our algorithm yields a significantly better performance. [ABSTRACT FROM AUTHOR]