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This paper brieffy overviews some recent and very fresh results on a rather new class of dynamic optimization problems governed by the so-called sweeping (Moreau) processes with controlled moving sets. Uncontrolled sweeping processes have been known in dynamical systems and applications starting from 1970s while control problems for them have drawn attention of mathematicians, applied scientists, and practitioners quite recently. We discuss here such problems and major results achieved in their theory and applications. [ABSTRACT FROM AUTHOR]

In this tutorial note we consider the class of non-convex semi-infinite optimization problems which are defined by (one or) finitely many objective functions as well as infinitely many constraints in a finitedimensional space. We present an overview of recent results on the so-called p-power transformation which changes the original problem equivalently locally around a solution point. This transformation is a convexification procedure for the Lagrangian where the functions in the original problem are substituted by their p-th powers. As a consequence, the convexity of the so-transformed Lagrangian allows the application of local duality theory and corresponding solution methods locally around this solution point of the original problem. [ABSTRACT FROM AUTHOR]

A widely used strategy in the design of block ciphers is the generation of key-dependent substitution boxes (S-Boxes), i.e., secret S-Boxes that depend randomly on the cipher key, guaranteeing security due to the uncertainty provided by the randomness of the S-Box during the encryption process and not due to its cryptographic properties. On the other hand, in the specialized literature there are several methods to construct cryptographically strong S-Boxes to be used in block ciphers, however, the S-Boxes built by such methods are fixed and need to be generated beforehand. Inspired by finding key-dependent S-Boxes with good cryptographic properties, we present in this paper a new strategy to generate key-dependent and cryptographically strong 8-bit S-Boxes from 4-bit permutations and heuristic search. [ABSTRACT FROM AUTHOR]

Due to the complex disjunctive structure of mathematical programs with complementarity constraints (MPCC), parametric approaches are used to overcome this difficulty. The underlying idea is to solve a program depending on the real parameter τ ≥ 0, where τ = 0 corresponds to the original MPCC program. The paper considers seven approaches: two based on smoothing the complementarity constraints and the other five, on their regularisation. We consider the point-to-set functions that, for each value of the parameter τ, define the set of feasible solutions and the set of optimal solution of the parametric problems they define. We study the distance between the feasible sets and the set of minimisers of the parametric program for τ going to zero. [ABSTRACT FROM AUTHOR]

In this paper, authors consider optimal replenishing strategies for constant demand in various financial scenarios by considering non-instantaneous deteriorating item under the progressive trade credit policy. The aim of this work is to develop a cost function for various situations depending on the trade credit period in economic environment. An algorithm is established to obtain the average cost, the replenishment time and the optimal order quantity. A thorough sensitivity analysis was carried out to assess the importance of the model. [ABSTRACT FROM AUTHOR]

In this paper, we have taken under considerations two auxiliary variables used as stratification variables and single study variable. For obtaining the optimum strata boundaries, we have solved the non linear equations using dynamic programming that resulted in more efficient results rather than obtained on using single study variable and single auxiliary variable. The proposed method is illustrated by using different distributions followed by stratification variables. [ABSTRACT FROM AUTHOR]

This paper is a production integrated inventory model between manufacturer and retailer with quadratic demand and time dependent deterioration. Paper also considers effect of inflation on total cost. Manufacturer offers lot size dependent ordering cost to boost higher orders as well as it decreases manufacturer's inventory holding cost significantly. Total cost of model is obtained using both classical optimization technique and genetic algorithm. Results clearly show that GA has succeeded in obtaining global minimum whereas classical method has stuck with local minimum. For using classical optimization technique we have used Maple 18 whereas for genetic algorithm we have used MATLAB R2013a.The optimal solution of this model is illustrated using numerical example. Sensitivity for inflation and other parameters of demand has been carried out to analyse their effect on total cost. This paper will encourage researchers involve in inventory and supply chain management to optimize complex problems using different evolutionary search algorithm in order to reach to global optimum. [ABSTRACT FROM AUTHOR]

This paper presents an adaptation of the Reinforcement Learning approach known as Q-Learning to solve a scheduling problem that comes from industry. The problem studied in this paper is known as the Hybrid-Flexible Flow Shop Scheduling where a variety of constraints are taken into account. These include precedence constraints, sequence dependent setup times (which can be anticipatory and non-anticipatory) along with machine lags, machine eligibility and release times. This problem mixes the features of regular Flow Shop and parallel machine problems by considering stages with several unrelated parallel machines, where stage skipping might occur, i.e., not all stages must be visited by all the jobs. This version has been proved strongly NP- hard and the objective is to determine a schedule that minimizes the maximum completion time (makespan or Cmax). The effectiveness of the proposed algorithm is empirically evaluated through several standard benchmarks problems and the solutions are compared against other high performing existing algorithm. The results shown that the proposed algorithm is very competitive for the studied problem. [ABSTRACT FROM AUTHOR]

This paper deals with approximate solutions of an optimization problem with interval-valued objective function. Four types of approximate solution concepts of the problem are proposed by considering the partial ordering LU on the set of all closed and bounded intervals. We show that these solutions exist under very weak conditions. Under suitable constraint qualifications, we derive Karush-Kuhn-Tucker necessary and sufficient optimality conditions for convex interval-valued optimization problems. [ABSTRACT FROM AUTHOR]

In this paper, a new Cellular Estimation Bayesian Algorithm for discrete optimization problems is presented. This class of stochastic optimization algorithm with learning from the structure and parameters of local populations are based on independence test and decentralized populations scheme, which can reduce the number of function evaluations solving for discrete optimization problems. The experimental results showed that this proposal reduces the number of evaluations in the search of the optimal for a benchmark discrete function with respect to other approaches of the literature. Also, it achieved better performance than them. [ABSTRACT FROM AUTHOR]

Multiple Sequence Alignment (MSA) is one of the main topics in the in bioinformatics domain, consists finding an optimal alignment for three or more biological sequences with the number maximum of conserved zones or totally aligned columns. Different scores to assess the quality of the alignments have been proposed, so the problem can be formulated and resolved as a Multi-Objective Optimization Problem (MOP). For this reason, in this paper we present a Multi- Objective approach applied to MSA. We have considered state-of-the-art optimization algorithms aimed at solving different formulations of the MSA: NSGAII, NSGA-III, SPEA2, MOCell, SMS-EMOA, MOEA/D and GWASF-GA. Furthermore we have considered some popular metrics as objectives to be optimized: The weighted Sum-Of-Pairs with a ne gap penalties (wSOP), the Totally Aligned Columns (TC), STRIKE and BaliScore. Finally we have described the main features of our software jMetalMSA, a Multi-Objective optimization software tool applied to MSA problem and illustrated a working example for experimentations purposes. [ABSTRACT FROM AUTHOR]

Proper management of projects has become an element of vital importance for the solution to problems of society. In this context, there are two main objectives to be solved: the construction of project plans which may comply with the constraints of the problem and the optimization in terms of cost and time of those plans. To this end, all activities of project must be organized in such way that the constraints related to the precedence among them and the availability of renewable and non-renewable resources at each instant of time are met. The aim of this paper is to present an Estimation of Distribution Algorithm (EDA), which incorporates the handling of the constraints in the probabilistic model, for the construction of optimal or quasi-optimal project plans. For the validation of the algorithm, authors used both the PSPLib database repository for the development of the scheduling research and the databases of projects of the Repository for Research in Project Management, University of Infomatic Sciences. [ABSTRACT FROM AUTHOR]

A Multi-Level Programming Problem (MLPP) is a hierarchical optimization problem where the constraint region of the first level is implicitly determined by the other optimization problems. In this paper, an integer multi-level programming problem is considered. At each level, there are multiple objective functions which are linear fractional and the feasible region is assumed to be a convex polyhedron. Here, the variables are bounded. An algorithm is developed for ranking and scanning the set of feasible solutions. These ranked solutions are used to find the efficient solution of Multi- Level Linear Fractional Programming Problem (MLLFPP). An example is illustrated and solved using LINGO 17. [ABSTRACT FROM AUTHOR]

In the field of service management plays a decisive role the location of the facilities to improve the quality of services. The maximal covering location problem allows locating a known number of facilities in order to maximize the demand covered. An important aspect to take into account is the varying of demand of the nodes with respect to the time (multi-period model). In addition, each facility could be of different types. A model that takes into account the existence of different types of facilities in order to cover the demand in multi-period environments has not been found in the literature. In this paper we propose a new generalization of the dynamic maximal covering location problem where different types of facilities (with different radius of coverage) could be open in each location. In this work we used the model on case study with the objective to locate the police patrol. [ABSTRACT FROM AUTHOR]

This paper is devoted to vector-valued optimization problems in Banach spaces whose objective functions are cone-convex and the feasible sets are not assumed to be convex. By means of a well-known nonlinear scalarizing function and the oriented distance function, we derive optimality conditions for weak Pareto solutions and (ϵ; e)-Pareto solutions in terms of abstract subdi erentials and the Clarke subdi erential. [ABSTRACT FROM AUTHOR]

The main objective of this work is the illustration of textual statistical analysis in confronting of the speeches of three great theorists of the world economy; it is aimed to review the emphasis from semantic segments that characterize the speech of each one. In this study, through a correspondence analysis, textual variables from the selected papers were identified, which are presented as characterizing the main concepts that establish patterns in each discourse. The results show the power of this methodology for applications in economics. [ABSTRACT FROM AUTHOR]

In recent years, the development of multi-classifier systems has become an active research field. A multi-classifier system is an ensemble of classification algorithms whose individual outputs are fused together for better accuracy and interpretability. An important aspect when designing such systems is related to the heterogeneity of the building blocks (classifiers) that make up the ensemble, since previous studies have uncovered that a more diverse ensemble often boosts up the overall classification power. Some statistical measures can be used to estimate how diverse the classifier ensembles are; they are called diversity measures. Another issue to be considered is the number of individual classifiers included in the model: the lower the number of classifiers, the simpler the resulting system. In general terms, the parsimony principle is highly desired in such ensembles, since a bulky ensemble will also be a very time-consuming model. Finding the minimal subset of individual classifiers that brings about the best system performance can be posed as a combinatorial optimization problem. In this paper, we address the problem of building multi-classifiers systems from the perspective of Ant Colony Optimization (ACO), a widely popular and effective metaheuristic optimization algorithm. The main reason behind the use of ACO lies on its strong ability to solve entangled combinatorial optimization problems. An empirical analysis is included to statistically validate the benefits of our proposal. [ABSTRACT FROM AUTHOR]

The location of facilities (antennas, ambulances, police patrols, etc.) has been widely study in the literature. The maximal covering location problem aims at locating the facilities in such positions that maximizes certain notion of coverage. The model of the MCLP and the solutions obtained as a result of applying metaheuristics are difficult to understand for non-expert users in the areas of optimization. In this sense, this paper aims to propose the development of software to model and solve the problem MCLP, as well as data management and visualization of the solutions obtained following the application of metaheuristics algorithms implemented with the BiCIAM framework. For the management of the problem data we used combinations based on GIS. [ABSTRACT FROM AUTHOR]

Many real-life scenarios can be modeled as Dynamic Optimization Problems (DOPs), which demand for finding optimal solution over time. From the viewpoint of metaheuristics methods, DOPs have been extensively addressed over the last two decades. One important issue in this context is how to assess the algorithm performance. Most of current proposals rely on single information from data, which limits the notion about the overall performance of the algorithm. So, in order to contribute to this issue, in this paper we propose a new performance measure for algorithm assessment in evolutionary dynamic optimization. We derived our proposal from what we considered as effectiveness in dynamic environments. Different from other existing measures, our proposal involve not only the accuracy, but also the time (efficiency) of the algorithm. In order to illustrate its usefulness and relationship with other literature measures an experimental analysis was conducted. Results show that the proposed measure can be suitable employed in typical experimentation scenarios and offers new information about the algorithms performance. [ABSTRACT FROM AUTHOR]

When we face an optimization problem whose de finition (in some aspect) changes over the time, we are in the presence of a Dynamic Optimization Problem (DOP). The aspects that can change are the objective function, the variables' domain, the appearance/disappearance of variables or constraints, etc. This paper aims at providing a first introduction to those who are interested in the topic. Concretely, we present the DOPs, the most common performance measures as well as the methods used to solve them. We also briefly describe some of the most recent reviews and comment some current challenges and research opportunities in DOPs. [ABSTRACT FROM AUTHOR]

Large scale continuous optimization problems have become increasingly common in real-world problems. The resolutions of these are computationally expensive, so the use of scalable and efficient algorithms is of particular interest. In this paper is proposed a hybrid algorithm, VMODE, which results from the combination of DE algorithm, known for its simplicity and efficiency and VMO, a population-based algorithm with encouraging results in continuous optimization. A comparison among the three algorithms is done using the 15 proposed functions for CEC-2013 (Special Session and Competition on Large-Scale Global Optimization) demonstrating the superiority of the algorithm VMODE. [ABSTRACT FROM AUTHOR]