Optimización a gran escala usando metaheurísticas

Este proyecto de investigación se enfoca en la resolución de problemas de optimización a gran escala utilizando nuevas técnicas metaheurísticas, así como también su hibridación con las ya existentes. Una de las líneas de investigación analiza el efecto de reemplazar el método para crear nuevas soluciones en el algoritmo artificial bee colony por operadores de recombinación. Otra de las líneas de investigación se enfoca en la resolución del problema flexible job shop scheduling (NP-hard), presente en ambientes fabriles, porque tiene que asignar cada operación a la máquina apropiada además de secuenciar las operaciones en las máquinas. Debido a esta complejidad, las metaheurísticas se convirtieron en la mejor opción para resolver en la práctica este problema. Una tercera línea de investigación apunta a la resolución del problema de diseño de redes de distribución de agua, mediante el uso de metaheurísticas como Simulated Annealing y Cuckoo Search. Por último, una línea de investigación se orienta a la utilización de la metaheurística basada en la migración de las aves en el problema de ruteo vehicular con capacidad, el cual es reconocido por su incidencia en el mundo de los negocios y por la dificultad para resolverlo.Eje: Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Optimización a gran escala usando metaheurísticas

Este proyecto de investigación se enfoca en la resolución de problemas de optimización a gran escala utilizando nuevas técnicas metaheurísticas, así como también su hibridación con las ya existentes. Una de las líneas de investigación analiza el efecto de reemplazar el método para crear nuevas soluciones en el algoritmo artificial bee colony por operadores de recombinación. Otra de las líneas de investigación se enfoca en la resolución del problema flexible job shop scheduling (NP-hard), presente en ambientes fabriles, porque tiene que asignar cada operación a la máquina apropiada además de secuenciar las operaciones en las máquinas. Debido a esta complejidad, las metaheurísticas se convirtieron en la mejor opción para resolver en la práctica este problema. Una tercera línea de investigación apunta a la resolución del problema de diseño de redes de distribución de agua, mediante el uso de metaheurísticas como Simulated Annealing y Cuckoo Search. Por último, una línea de investigación se orienta a la utilización de la metaheurística basada en la migración de las aves en el problema de ruteo vehicular con capacidad, el cual es reconocido por su incidencia en el mundo de los negocios y por la dificultad para resolverlo.Eje: Agentes y Sistemas Inteligentes.Red de Universidades con Carreras en Informátic

Investigation of the effect of feeding period in honey bee algorithm

In the study, it was investigated the ejaculation ability and semen quality of drones, according to feeding with pollen in different periods. In the first step of the study, 16 %, 32 %, 47 %, 63 %, 79 %, and 100 % feeding periods were applied to the drones, for investigating the effect on ejaculation ability, and the semen quality of drones was investigated. While investigating these feeding period effects “0-1”, bonded, and unbounded knapsack optimization problems were used. After the most effective feeding period was determined, this period was applied to the traveling salesman and liquid storage tank problems in the second step of the study. In the analysis of the traveling salesman problem, it was determined the shortest way between two cities. Analysis of the liquid storage tank problem, it was determined the minimum connector areas. As a result, the analysis results showed that the performance of the artificial bee colony algorithm is very good while solving too complex engineering optimization problems

Angle Modulated Artificial Bee Colony Algorithms for Feature Selection

Optimal feature subset selection is an important and a difficult task for pattern classification, data mining, and machine intelligence applications. The objective of the feature subset selection is to eliminate the irrelevant and noisy feature in order to select optimum feature subsets and increase accuracy. The large number of features in a dataset increases the computational complexity thus leading to performance degradation. In this paper, to overcome this problem, angle modulation technique is used to reduce feature subset selection problem to four-dimensional continuous optimization problem instead of presenting the problem as a high-dimensional bit vector. To present the effectiveness of the problem presentation with angle modulation and to determine the efficiency of the proposed method, six variants of Artificial Bee Colony (ABC) algorithms employ angle modulation for feature selection. Experimental results on six high-dimensional datasets show that Angle Modulated ABC algorithms improved the classification accuracy with fewer feature subsets

Optimising the climate resilience of shipping networks

Climate catastrophes (e.g. hurricane, flooding and heat waves) are generating increasing impact on port operations and hence configuration of shipping networks. This paper formulates the routing problem to optimise the resilience of shipping networks, by taking into account the disruptions due to climate risks to port operations. It first describes a literature review with the emphasis on environmental sustainability, port disruptions due to climate extremes and routing optimisation in shipping operations. Second, a centrality assessment of port cities by a novel multi-centrality-based indicator is implemented. Third, a climate resilience model is developed by incorporating the port disruption days by climate risks into shipping route optimisation. Its main contribution is constructing a novel methodology to connect climate risk indices, centrality assessment, and shipping routing to observe the changes of global shipping network by climate change impacts

Digital Filter Design Using Improved Artificial Bee Colony Algorithms

Digital filters are often used in digital signal processing applications. The design objective of a digital filter is to find the optimal set of filter coefficients, which satisfies the desired specifications of magnitude and group delay responses. Evolutionary algorithms are population-based meta-heuristic algorithms inspired by the biological behaviors of species. Compared to gradient-based optimization algorithms such as steepest descent and Newton’s like methods, these bio-inspired algorithms have the advantages of not getting stuck at local optima and being independent of the starting point in the solution space. The limitations of evolutionary algorithms include the presence of control parameters, problem specific tuning procedure, premature convergence and slower convergence rate. The artificial bee colony (ABC) algorithm is a swarm-based search meta-heuristic algorithm inspired by the foraging behaviors of honey bee colonies, with the benefit of a relatively fewer control parameters. In its original form, the ABC algorithm has certain limitations such as low convergence rate, and insufficient balance between exploration and exploitation in the search equations. In this dissertation, an ABC-AMR algorithm is proposed by incorporating an adaptive modification rate (AMR) into the original ABC algorithm to increase convergence rate by adjusting the balance between exploration and exploitation in the search equations through an adaptive determination of the number of parameters to be updated in every iteration. A constrained ABC-AMR algorithm is also developed for solving constrained optimization problems.There are many real-world problems requiring simultaneous optimizations of more than one conflicting objectives. Multiobjective (MO) optimization produces a set of feasible solutions called the Pareto front instead of a single optimum solution. For multiobjective optimization, if a decision maker’s preferences can be incorporated during the optimization process, the search process can be confined to the region of interest instead of searching the entire region. In this dissertation, two algorithms are developed for such incorporation. The first one is a reference-point-based MOABC algorithm in which a decision maker’s preferences are included in the optimization process as the reference point. The second one is a physical-programming-based MOABC algorithm in which physical programming is used for setting the region of interest of a decision maker. In this dissertation, the four developed algorithms are applied to solve digital filter design problems. The ABC-AMR algorithm is used to design Types 3 and 4 linear phase FIR differentiators, and the results are compared to those obtained by the original ABC algorithm, three improved ABC algorithms, and the Parks-McClellan algorithm. The constrained ABC-AMR algorithm is applied to the design of sparse Type 1 linear phase FIR filters of filter orders 60, 70 and 80, and the results are compared to three state-of-the-art design methods. The reference-point-based multiobjective ABC algorithm is used to design of asymmetric lowpass, highpass, bandpass and bandstop FIR filters, and the results are compared to those obtained by the preference-based multiobjective differential evolution algorithm. The physical-programming-based multiobjective ABC algorithm is used to design IIR lowpass, highpass and bandpass filters, and the results are compared to three state-of-the-art design methods. Based on the obtained design results, the four design algorithms are shown to be competitive as compared to the state-of-the-art design methods

Performance evaluation of artificial bee colony optimization and new selection schemes

The artificial bee colony optimization (ABC) is a population-based algorithm for function optimization that is inspired by the foraging behavior of bees. The population consists of two types of artificial bees: employed bees (EBs) which scout for new, good solutions and onlooker bees (OBs) that search in the neighborhood of solutions found by the EBs. In this paper we study in detail the influence of ABC's parameters on its optimization behavior. It is also investigated whether the use of OBs is always advantageous. Moreover, we propose two new variants of ABC which use new methods for the position update of the artificial bees. Extensive empirical tests were performed to compare the new variants with the standard ABC and several other metaheuristics on a set of benchmark functions. Our findings show that the ideal parameter values depend on the hardness of the optimization goal and that the standard values suggested in the literature should be applied with care. Moreover, it is shown that in some situations it is advantageous to use OBs but in others it is not. In addition, a potential problem of the ABC is identified, namely that it performs worse on many functions when the optimum is not located at the center of the search space. Finally it is shown that the new ABC variants improve the algorithm's performance and achieve very good performance in comparison to other metaheuristics under standard as well as hard optimization goals. © 2011 Springer-Verlag.SCOPUS: ar.jinfo:eu-repo/semantics/publishe

XX Workshop de Investigadores en Ciencias de la Computación - WICC 2018 : Libro de actas

Actas del XX Workshop de Investigadores en Ciencias de la Computación (WICC 2018), realizado en Facultad de Ciencias Exactas y Naturales y Agrimensura de la Universidad Nacional del Nordeste, los dìas 26 y 27 de abril de 2018.Red de Universidades con Carreras en Informática (RedUNCI