Improving Artificial Bee Colony Algorithm with Evolutionary Operators

In this paper, we analyze the effect of replacing the method to create new solutions in artificial bee colony algorithm by recombination operators. Since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm.Eje: XVIII Workshop de Agentes y Sistemas Inteligentes (WASI).Red de Universidades con Carreras en Informática (RedUNCI

Algoritmo de colonia de abejas artificiales hibridado con algoritmos evolutivos

In this paper, we design, implement, and analysis the replacement of the method to create new solutions in artificial bee colony algorithm by recombination operators, since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All the analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm. Computational results and comparisons show that three of the six proposed algorithms are very competitive with the traditional bee colony algorithm.En este trabajo, se ha diseñado, implementado y analizado el reemplazo del método para crear nuevas soluciones en algoritmos basados en colonia de abejas artificiales por operadores de recombinación, ya que el método original es similar al proceso de recombinación usado en los algoritmos evolutivos. Para cumplir con este propósito, se presenta una investigación sistemática del efecto de usar seis operadores de recombinación distintos en el procedimiento llevado a cabo por la abeja empleada. Para la experimentación se utilizan casos de pruebas complejos, habitualmente utilizados en la literatura. Los resultados obtenidos sugieren que el método generador de nuevas fuentes de comida afecta el desempeño del algoritmo. A partir del análisis y comparaciones de los resultados, se observa que tres de las seis propuestas algorítmicas son competitivas con respecto al algoritmo basado en colonia de abejas tradicional.Facultad de Informátic

Algoritmo de colonia de abejas artificiales hibridado con algoritmos evolutivos

In this paper, we design, implement, and analysis the replacement of the method to create new solutions in artificial bee colony algorithm by recombination operators, since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All the analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm. Computational results and comparisons show that three of the six proposed algorithms are very competitive with the traditional bee colony algorithm.En este trabajo, se ha diseñado, implementado y analizado el reemplazo del método para crear nuevas soluciones en algoritmos basados en colonia de abejas artificiales por operadores de recombinación, ya que el método original es similar al proceso de recombinación usado en los algoritmos evolutivos. Para cumplir con este propósito, se presenta una investigación sistemática del efecto de usar seis operadores de recombinación distintos en el procedimiento llevado a cabo por la abeja empleada. Para la experimentación se utilizan casos de pruebas complejos, habitualmente utilizados en la literatura. Los resultados obtenidos sugieren que el método generador de nuevas fuentes de comida afecta el desempeño del algoritmo. A partir del análisis y comparaciones de los resultados, se observa que tres de las seis propuestas algorítmicas son competitivas con respecto al algoritmo basado en colonia de abejas tradicional.Facultad de Informátic

Algoritmo de colonia de abejas artificiales hibridado con algoritmos evolutivos

In this paper, we design, implement, and analysis the replacement of the method to create new solutions in artificial bee colony algorithm by recombination operators, since the original method is similar to the recombination process used in evolutionary algorithms. For that purpose, we present a systematic investigation of the effect of using six different recombination operators for real-coded representations at the employed bee step. All the analysis is carried out using well known test problems. The experimental results suggest that the method to generate a new candidate food position plays an important role in the performance of the algorithm. Computational results and comparisons show that three of the six proposed algorithms are very competitive with the traditional bee colony algorithm.En este trabajo, se ha diseñado, implementado y analizado el reemplazo del método para crear nuevas soluciones en algoritmos basados en colonia de abejas artificiales por operadores de recombinación, ya que el método original es similar al proceso de recombinación usado en los algoritmos evolutivos. Para cumplir con este propósito, se presenta una investigación sistemática del efecto de usar seis operadores de recombinación distintos en el procedimiento llevado a cabo por la abeja empleada. Para la experimentación se utilizan casos de pruebas complejos, habitualmente utilizados en la literatura. Los resultados obtenidos sugieren que el método generador de nuevas fuentes de comida afecta el desempeño del algoritmo. A partir del análisis y comparaciones de los resultados, se observa que tres de las seis propuestas algorítmicas son competitivas con respecto al algoritmo basado en colonia de abejas tradicional.Facultad de Informátic

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

An efficient discrete artificial bee colony algorithm for the blocking flow shop problem with total flowtime minimization

This paper presents a high performing Discrete Artificial Bee Colony algorithm for the blocking flow shop problem with flow time criterion. To develop the proposed algorithm, we considered four strategies for the food source phase and two strategies for each of the three remaining phases (employed bees, onlookers and scouts). One of the strategies tested in the food source phase and one implemented in the employed bees phase are new. Both have been proved to be very effective for the problem at hand. The initialization scheme named HPF2(¿, µ) in particular, which is used to construct the initial food sources, is shown in the computational evaluation to be one of the main procedures that allow the DABC_RCT to obtain good solutions for this problem. To find the best configuration of the algorithm, we used design of experiments (DOE). This technique has been used extensively in the literature to calibrate the parameters of the algorithms but not to select its configuration. Comparing it with other algorithms proposed for this problem in the literature demonstrates the effectiveness and superiority of the DABC_RCTPeer ReviewedPostprint (author’s final draft

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

Developing an Agent Based Heuristic Optimisation System for Complex Flow Shops with Customer-Imposed Production Disruptions

The study of complex manufacturing flow-shops has seen a number of approaches and frameworks proposed to tackle various production-associated problems. However, unpredictable disruptions, such as change in sequence of order, order cancellation and change in production delivery due time, imposed by customers on flow-shops that impact production processes and inventory control call for a more adaptive approach capable of responding to these changes. In this research work, a new adaptive framework and agent-based heuristic optimization system was developed to investigate the disruption consequences and recovery strategy. A case study using an Original Equipment Manufacturer (OEM) production process of automotive parts and components was adopted to justify the proposed system. The results of the experiment revealed significant improvement in terms of total number of late orders, order delivery time, number of setups and resources utilization, which provide useful information for manufacturer’s decision-making policies.

A modified memetic algorithm with an application to gene selection in a sheep body weight study

Selecting the minimal best subset out of a huge number of factors for influencing the response is a fundamental and very challenging NP-hard problem because the presence of many redundant genes results in over-fitting easily while missing an important gene can more detrimental impact on predictions, and computation is prohibitive for exhaust search. We propose a modified memetic algorithm (MA) based on an improved splicing method to overcome the problems in the traditional genetic algorithm exploitation capability and dimension reduction in the predictor variables. The new algorithm accelerates the search in identifying the minimal best subset of genes by incorporating it into the new local search operator and hence improving the splicing method. The improvement is also due to another two novel aspects: (a) updating subsets of genes iteratively until the no more reduction in the loss function by splicing and increasing the probability of selecting the true subsets of genes; and (b) introducing add and del operators based on backward sacrifice into the splicing method to limit the size of gene subsets. Additionally, according to the experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms. Moreover, the mutation operator is replaced by it to enhance exploitation capability and initial individuals are improved by it to enhance efficiency of search. A dataset of the body weight of Hu sheep was used to evaluate the superiority of the modified MA against the genetic algorithm. According to our experimental results, our proposed optimizer can obtain a better minimal subset of genes with a few iterations, compared with all considered algorithms including the most advanced adaptive best-subset selection algorithm