The bee colony-inspired algorithm (BCiA)

Häckel, Sascha; Dippold, Patrick

doi:10.1145/1569901.1569906

Cited by 14 publications

(2 citation statements)

References 19 publications

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“…They can be categorised into three groups: biologically-based inspiration, e.g. genetic algorithm or GA [2], memetics algorithm or MAs [2], shuffled frog leaping algorithm or SFLA [2], firefly algorithm or FFA [3], bees algorithm or BEES [4], harmony search algorithm or HSA [5], neural network or NN [6], ant colony optimisation or ACO [7], evolutionary programming or EP [8], differential evolution or DE [9] and particle swarm optimisation or PSO [10]. Moreover, there are some with the socially-based inspiration, e.g.…”

Section: Introductionmentioning

confidence: 99%

Continuous Stirred Tank Reactor Optimisationvia Simulated Annealing, Firefly and Ant ColonyOptimisation Elements on the Steepest Ascent

Luangpaiboon¹

2011

IJMLC

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Abstract-The objective of this work is to make use of conventional response surface methodologies and basic elements from metaheuristic algorithms in the design of influential variables for engineering systems. A method of steepest ascent and its integrated approaches with simulated annealing, firefly and ant colony optimisation algorithms, are compared on a simulated continuous stirred tank reactor or CSTR with various levels of signal noise. These metaheuristics contain the complicatedness in terms of their parameters. An additional series of computational experiments were conducted and analysed in terms of the minimax and mean squared error performance measures including Taguchi's signal to noise ratio. Proper levels of these parameters are analysed to recommend the best parameter choices. On the experimental results of all the algorithms with the preferable levels of parameters, the method of steepest ascent seems to be the most efficient on the CSTR surface at the lower levels of noise. However, the integrated approaches with all simulated annealing, firefly and ant colony optimisation elements work well when the standard deviation of the noise is at higher levels. Although the average, the standard deviation of the greatest actual concentration of the product and percentage of sequences ended at the optimum from the integrated algorithms with simulated annealing and ant colony optimisation seem to be better, they need more average design points, especially with ant colony optimisation element, to converge to the optimum when compared.

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Section: Introductionmentioning

confidence: 99%

Continuous Stirred Tank Reactor Optimisationvia Simulated Annealing, Firefly and Ant ColonyOptimisation Elements on the Steepest Ascent

Luangpaiboon¹

2011

IJMLC

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“…Later, the same authors presented an application of the BA to the optimization of neural networks for wood failure detection (Pham & Koc, 2006). More recently, Häckel and Dippold (2009) developed an algorithm inspired in bee colony for the vehicle routing problem with time windows, a very well-known NP-hard combinatorial problem. According to Mishra (2007), the algorithms mentioned before have an inherent probabilistic nature and thus may not always obtain best solutions with certainty.…”

Section: Introduction and Related Workmentioning

confidence: 99%

An Improved Artificial Bee Colony Algorithm for the Object Recognition Problem in Complex Digital Images Using Template Matching

Chidambaram

Lopes

2010

International Journal of Natural Computing Research

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In this paper, the authors present an improved Artificial Bee Colony Algorithm (ABC) for the object recognition problem in complex digital images. The ABC is a new metaheuristics approach inspired by the collective foraging behavior of honey bee swarms. The objective is to find a pattern or reference image (template) of an object somewhere in a target landscape scene that may contain noise and changes in brightness and contrast. First, several search strategies were tested to find the most appropriate. Next, many experiments were done using complex digital grayscale and color images. Results are analyzed and compared with other algorithms through Pareto plots and graphs that show that the improved ABC was more efficient than the original ABC.

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Introduction to Computational Intelligence

Xing

Gao²

2013

Innovative Computational Intelligence: A Rough Guide to 134 Clever Algorithms

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In this chapter, we introduce some general knowledge relative to the realm of computational intelligence (CI). The desirable merits of these intelligent algorithms and their initial successes in many domains have inspired researchers (from various backgrounds) to continuously develop their successors. Such truly interdisciplinary environment of the research and development provides more and more rewarding opportunities for scientific breakthrough and technology innovation. We first introduce some historical information regarding CI in Sect. 1.1. Then, the organizational structures are detailed in Sect. 1.2. Finally, Sect. 1.3 summarises in this chapter.

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The bee colony-inspired algorithm (BCiA)

Cited by 14 publications

References 19 publications

Continuous Stirred Tank Reactor Optimisationvia Simulated Annealing, Firefly and Ant ColonyOptimisation Elements on the Steepest Ascent

Continuous Stirred Tank Reactor Optimisationvia Simulated Annealing, Firefly and Ant ColonyOptimisation Elements on the Steepest Ascent

An Improved Artificial Bee Colony Algorithm for the Object Recognition Problem in Complex Digital Images Using Template Matching

Introduction to Computational Intelligence

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