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In today's times, the majority of investors are willing easy gains, and the easiest way is to invest in productive domains, even if the risks they have to take are very high, but these are not the last problem. The real problem for investors is to take the right decision and, for that, they have to build forecast models, to make some assumptions for default variables, which are difficult to understand. To refine the prediction process, researchers have developed hybrid models for forecasting. A famous class of all these techniques is in the metaheuristics domain. The purpose of this paper is to present the hybridization process and hybrid algorithms. More over, another goal is to reveal some possibilities of metaheuristics, such as hybridization of algorithms. [ABSTRACT FROM AUTHOR]

One of graph optimization's fundamental and most challenging problems is determining the maximum set of unconnected vertices in a graph, called the maximum independent set problem. This problem consists of finding the largest independent set in a graph, where an independent set is a set of vertices such that no two vertices are adjacent. This paper presents a new artificially generated algorithm for the maximum independent set problem. The new algorithm is generated by the automatic generation of algorithms, a technique that allows the construction of new hybrid algorithms, taking advantage of existing algorithms. Thus, the automatic generation of algorithms combines basic heuristics for the problem, a tabu search method selected from the literature, and an exact method that solves the problem's mathematical formulation working for a limited computational time. With these components, the space of possible algorithms is traversed by employing genetic programming. Algorithms of small sizes are generated to study their structure and discover new algorithmic combinations. Then, we select the algorithm that finds solutions with the best computational performance among all the generated algorithms. This best algorithm is compared with three state-of-the-art algorithms for the problem, presenting the best computational performance for the 131 instances in the literature. • Artificially generated algorithms for solving the maximum independent set problem. • Algorithms are trained with different groups of instances. • Algorithms generated combine basic heuristics, a tabu search, and an exact method. • An artificial algorithm outperforms all generated ones in computational performance. • The best generated algorithm outperforms the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In this paper we propose an optimization framework for multiple deployment of PON in a wide region with very large number of users, with different bit rate demands, serviced by many central offices, as it may practically happen in a large city that plans a massive introduction of Fiber to the Home technologies using PON. We propose an algorithm called Optimal Topology Search (OTS), which is based on a set of heuristic approaches, capable of performing an optimal dimensioning of multiple PON deployments for a set of central offices (CO), including an optimal distribution of users among the CO. The set of heuristics integrated in OTS permit the efficient clustering of users for each CO, depending on their location and the bit rate demanded by them. It also permits the definition of optimal routes for optical cables and the allocation of branching devices. Taking into account hardware capacity restrictions and physical layer restrictions, we obtained solutions for different types of standardized PON technologies, like GPON, XGPON and NGPON2 as well as for future UDWDM-PON. We evaluate the optimal network deployment in a series of different minimum guaranteed bit rate demand scenarios, employing realistic maps of a large city in order to compare costs and portrait some reference points for deciding in which scenario a specific technology constitutes the best choice. [ABSTRACT FROM AUTHOR]

• A myriad of static algorithms have been proposed for solving the problem that can be roughly categorized into three groups: approaches for generating optimal solutions (for smaller sizes problems), elaborate optimization techniques, and fast heuristics. • This paper proposes a framework, evaluation procedures, and a set of benchmarks for methodical comparisons of various algorithms for solving the PET-aware task-to-core scheduling problem. • The proposed performance measures assist in judiciously comparing these different algorithms and analyzing their results on a unified basis. • The goal is also to seek answers as to how good the Pareto-optimal algorithms are compared to fast heuristics tackling the same problem with the same assumptions. • The proposed framework in evaluating trade-offs and determining which application and target parameters affect the results (performance, energy consumed and temperature achieved) of these algorithms is useful. Extensive experimentation facilitates a comprehensive comparison of different kinds of algorithms among themselves as well as with optimal solutions obtained through Integer Linear Programming as a reference. The task-to-core scheduling problem for allocating tasks graphs using Dynamic Voltage and Frequency Scaling (DVFS) for achieving three objectives of performance, energy, and temperature (PET), poses algorithmic challenges as it involves conflicting goals and trade-offs. A myriad of static algorithms have been proposed for solving the problem. These algorithms can be roughly categorized into three groups: techniques that generate optimal solutions (for smaller sizes problems) for one objective, techinques that generate Pareto-optimal solutions, and fast heuristics. All of these techniques generate multi-dimensional results that are hardly intelligible. The assessment of these results requires new comparison methods and concise evaluation measures. This paper proposes a framework, evaluation procedures, and a set of benchmarks for methodical comparisons of various algorithms for solving the PET-aware task-to-core scheduling problem. The proposed performance measures assist in judiciously comparing these different algorithms and analyzing their results on a unified basis. The goal is also to seek answers as to how good the Pareto-optimal algorithms are compared to fast heuristics in tackling the same problem with the same assumptions. At the same time, we are interested in knowing how good Pareto-optimal and heuristics are when compared to the absolute optimal (at least for small sets of problems). The algorithms are multi-objective in nature and the problem and system parameters are numerous, which makes the comparison hard. Thus, the proposed framework in evaluating trade-offs and determining which application and target parameters affect the results (performance, energy consumed and temperature achieved) of these algorithms is highly useful. Extensive experimentation facilitates a comprehensive comparison of different kinds of algorithms among themselves as well as with optimal solutions obtained through Integer Linear Programming as a reference. [ABSTRACT FROM AUTHOR]

This paper considers the uniform machine scheduling problem with release dates so as to minimize the total completion time. The problem is known to be NP-hard in the strong sense, even when there is only a single machine. An intelligent scheduling algorithm, called ABISA , is proposed, in which agent technology is introduced to realize the robotization process of manufacturing scheduling by means of the intelligence of the machines. Semantic description of two kinds of agent is given, and token-ring mechanism is presented for agent coordination. A lower bound is derived which is used to evaluate the performance of the algorithms. By examining 1800 random problem instances, the algorithm shows an excellent performance in the solution quality. The results obtained by ABISA are better than the algorithms based on traditional heuristic rules, and are closer to the lower bound of the problem. [ABSTRACT FROM AUTHOR]

Abstract: The competitiveness of a container terminal is highly conditioned by the time that container vessels spend on it. The proper scheduling of the quay cranes can reduce this time and allows a container terminal to be more attractive to shipping companies. The goal of the Quay Crane Scheduling Problem (QCSP) is to minimize the handling time of the available quay cranes when performing the tasks of loading and unloading containers onto/from a container vessel. This paper proposes a hybrid Estimation of Distribution Algorithm with local search to solve the QCSP. This approach includes a priori knowledge about the problem in the initialization step to reach promising regions of the search space as well as a novel restarting strategy with the aim of avoiding the premature convergence of the search. Furthermore, an approximate evaluation scheme is applied in order to reduce the computational burden. Moreover, its performance is statistically compared with the best optimization method from the literature. Numerical testing results demonstrate the high robustness and efficiency of the developed technique. Additionally, some relevant components of the scheme are individually analyzed to check their effectiveness. [Copyright &y& Elsevier]

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This study develops a single phase algorithm for the fixed destination multi-depot multiple traveling salesman problem (MD-MTSP) with sub tour construction. First show the general model of the technical inspection and maintenance systems, where this problem usually emerges and present the most important participants in these systems, like the virtual logistic centres, experts, objects, repair companies, warehouses, suppliers. It propose a mathematical model of the system's object expert assignment with the constraints like experts minimum and maximum capacity, constraint on expert maximum and daily tour. In the next part it presents the developed evolutionary programming algorithm which solves the assignment, regarding the constraints with the usage of penalty functions in the algorithm. In the last part of the paper the convergence of the algorithm and the solving times are presented. [ABSTRACT FROM AUTHOR]

Copyright of Revista de Economia e Sociologia Rural is the property of Sociedade Brasileira de Economia e Sociologia Rural and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract: The task of marker optimization in clothing production is to eliminate pieces from a work order using an optimal sequence of markers and plies, where the work order is given as a matrix of colors by sizes, markers are vectors of sizes to be laid-out and cut together, and the number of plies determines how many pieces are eliminated from the work order each time. Although the optimality of a marker sequence can be determined in several ways, we consider minimum preparation cost as a key objective in clothing production. The traditional algorithms and the simple evolutionary algorithms used in marker optimization today have relied on minimizing the number of markers, which only indirectly reduces production costs. In this paper we propose a hybrid self-adaptive evolutionary algorithm (HSA-EA) for marker optimization that improves the results of the previous algorithms and successfully deals with the objective of minimum preparation cost. [Copyright &y& Elsevier]

Abstract: In this paper, we introduce a new optimization problem, the Minimum Cost Hop-and-root Constrained Forest Problem, which arises in the design of energy efficient Wireless Sensor Networks. An Integer Program, a heuristic solution approach and computational experiments with the proposed models and algorithms are presented. [Copyright &y& Elsevier]

Abstract: The optimal positioning of switches in a mobile communication network is an important task, which can save costs and improve the performance of the network. In this paper we propose a model for establishing which are the best nodes of the network for allocating the available switches, and several hybrid genetic algorithms to solve the problem. The proposed model is based on the so-called capacitated p-median problem, which have been previously tackled in the literature. This problem can be split in two subproblems: the selection of the best set of switches, and a terminal assignment problem to evaluate each selection of switches. The hybrid genetic algorithms for solving the problem are formed by a conventional genetic algorithm, with a restricted search, and several local search heuristics. In this work we also develop novel heuristics for solving the terminal assignment problem in a fast and accurate way. Finally, we show that our novel approaches, hybridized with the genetic algorithm, outperform existing algorithms in the literature for the p-median problem. [Copyright &y& Elsevier]

In Markov games, playing against non-stationary opponents with learning ability is still challenging for reinforcement learning (RL) agents, because the opponents can evolve their policies concurrently. This increases the complexity of the learning task and slows down the learning speed of RL agents. This paper proposes efficient use of rough heuristics to speed up policy learning when playing against concurrent learners. Specifically, we propose an algorithm that can efficiently learn explainable and generalized action selection rules by taking advantage of representations of quantitative heuristics and an opponent model with an eXtended classifier system (XCS) in zero-sum Markov games. A neural network is used to model the opponent from their behaviors and the corresponding policy is inferred for action selection and classifiers evolution. Due to the condition representation and the matching mechanism of XCS, the quantitative heuristics and the opponent model can provide guidance for action selection in states with similar feature representations. Besides, in case of multiple heuristic policies, we introduce the concept of Pareto optimality to comprehensively consider all the heuristic policies for action selection. Moreover, we analyze the influence of heuristic policies on the convergence of the algorithm. Furthermore, we introduce accuracy-based eligibility traces to further speed up classifiers evolution, i.e., in the reinforcement part, classifiers that can match historical traces are reinforced according to their accuracy. We demonstrate the advantages of the proposed algorithm over several benchmark algorithms in a soccer scenario and a thief-and-hunter scenario. [ABSTRACT FROM AUTHOR]