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In this paper, we introduce a simulated annealing algorithm for single objective, trans-dimensional optimization problems. Trans-dimensional optimization refers to a class of problems where candidate solutions can have different number of variables. For such problems, the existing optimization methods need to be run for various models (i.e. problems with fixed number of variables) extensively, which...
The isin-DANTE method is an hybrid meta-heuristic. In combines the evolutionary ant colony optimization (ACO) algorithms with a limited depth search. This depth search is based in the pheromone trails used by the ACO, which allows it to be oriented to the more promising areas of the search space. Some results are presented for the multiple objective k-degree spanning trees problem, proving the effectiveness...
Common evolutionary approaches to protein-ligand docking optimization use mutation operators based on Gaussian and Cauchy distributions, with local search hybrids. The choice of a local search method is important for an efficient algorithm. We investigate the impact of local search with mutation operators by performing a locality analysis. High locality means that small variations in the genotype...
The harmony search (HS) method is an emerging meta-heuristic optimization algorithm. In this paper, we propose two modified HS methods to deal with the uni-modal and multi-modal optimization problems. The first modified HS method is based on the fusion of the HS and differential evolution (DE) technique, namely, HS-DE. The DE is employed here to optimize the members of the HS memory. The second modified...
The joined travelling of vehicles is an important instrument of cost reduction in the innovative RailCab concept. While the problem of joining groups of vehicles into convoys and determining convoy routes can be easily understood as optimization problem, the distributed nature and large number of vehicles and stops inhibits the direct application of operations research methods and problems. In this...
To develop truly autonomous mobile robots, we propose to introduce internal rewards such as the desire for existence, specific curiosity, diversive curiosity, boredom, and novelty into reinforcement learning. They are expected to make mobile robots capable of behaving appropriately without being told what to do. Firstly, we propose to use multiple sources of rewards to endow mobile robots with ability...
In this paper we deal with the problem of automatically optimizing the gait of a robot for forward walking speed. Each different walking surface and/or the wear and tear of the robots determines the speed of the robot. This means that a specific gait for one surface may not be valid on another surface or even on the same surface some time later. Given a parametrized walk designed for the robot and...
This paper presents an enhanced version of the AED (Appropriate Executive Decisions) algorithm, which is based on biological immune system (BIS) and whose purpose is the generation of appropriate executive decisions aimed at business environments. A new metric has been incorporated to the algorithm and a larger and more representative database was used to train and validate results. Moreover, this...
One of the challenges of SOA is to deal with service matching which is uncertain and ambiguous. A service requester must be prepared to cope with situations where no required services are found or, on the other hand, multiple matching services are found. The paper proposes a formal model of service matching with incomplete information. The model is defined using set theory and description logics....
To explore the fusion of unstructured text data, the concept of entity networks has been proposed in many systems to facilitate analysis of entity relationship. Using appropriate visualization, entity networks could illustrate the relations of extracted entities. However, such a system generates too many entities and links from a single document, and many of them are trivial. With a complicated entity...
Hybrid models combine different technologies to obtain a product that shares their advantages and minimizes their deficiencies. The solutions given by a case-based system (CBS) rely on similar past experiences, which are commonly described in terms of both symbolic and continuous attributes. The nearest neighbor (NN) principle commonly followed to develop CBS for classification task proceeds from...
In this paper we propose a two-level MOEA to help on the sugarcane harvest decision support. This problem is multi-objective in nature, as it contains agronomical and logistic objectives considered simultaneously. Two different sets of heuristics were used during harvest decisions, namely crisp and fuzzy prioritization schema. They are both tested and compared here with regards to effective help to...
The article presents an adaptive hybrid planning system or the interactive solution of multi-objective vehicle routing problems. A general framework was built, being able to handle various components of general vehicle routing problems, e.g. the simultaneous consideration of six optimization criteria. Solutions are constructed and improved in real time allowing the user to adapt his articulated preference...
This paper presents an application of neural network interleaved training algorithm proposed in in the domain of chess. In order to use the referenced learning method a structure of metric space is introduced in the space of chess moves. Neural network is used as a classifier of a distance from a given move to the optimal one, leading to significant limitation of the set of moves potentially worth...
This paper introduces a weighted partitioning dynamic clustering algorithm for quantitative feature data based on adaptive euclidean distances. The proposed method is an iterative four-steps relocation algorithm involving the determination of the clusters representatives (prototypes), the weight of each individual, the distance associated to each cluster and the construction of the clusters, at each...
In this paper, we study a single objective extension of support vector machines for multicategory classification. Extending the dual formulation of binary SVMs, the algorithm looks for minimizing the sum of all the pairwise distances among a set of prototypes, each one constrained to one of the convex-hulls enclosing a class of examples. The final discriminant system is built looking for an appropriate...
The requirements for quality of service (QoS) of applications in networks are becoming stricter. Toward to solve the routing problem in IP networks, MPLS architecture allows to define explicit routes in the network. This paper describes a multi-objective heuristic approach to solve the routing problem in MPLS networks. An evolutionary multi-objective algorithm is proposed based on Dijkstra's shortest...
Wireless Sensor Networks (WSN) of Smart dust motes are becoming increasingly effective in environmental monitoring applications. In some applications, data gathered via WSN are only useful when combined with individual mote positions and time-stamps; if the motes are not static, it is important to find methods for their 3D location estimation from available RSSI signal data. Usually termed 'Localisation',...
We propose a self-adaptive hybrid evolutionary algorithm for the optimization of Morse clusters. The approach relies on a two-phase local optimization method to efficiently guide search. Individuals encode its own penalty settings and the algorithm evolves them simultaneously with the search for low energy clusters. Results show that the approach is efficient, as it is able to discover all optimal...
This article considers the bi-objective job shop scheduling problem in which the make span and the total tardiness of jobs are minimized. In order to find a set of dominant solutions, that is, an approximation of the Pareto optimal solutions, we propose three versions of a genetic algorithm with techniques like hybridization with local search, path relinking and elitism. The three versions of the...
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