Computational Chemotaxis in Micro Bacterial Foraging Optimization for High Dimensional Problems: A Comparative Study on Numerical Benchmark (original) (raw)
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2013 IEEE International Conference on Cybernetics (CYBCO), 2013
This paper first proposes a simple scheme for adapting the chemotactic step size of the Bacterial Foraging Optimization Algorithm (BFOA), and then this new adaptation and two very popular optimization techniques called Partic le Swarm Optimization (PSO) and Differential Evolution (DE) are coupled in a new hybrid approach named Adaptive Chemotactic Bacterial Swarm Foraging Optimization with Differential Evolution Strategy (ACBSFO_DES). This novel technique has been shown to overcome the problems of premature convergence and slow of both the classical BFOA and the other BFOA hybrid variants over several benchmark problems.
Soft Computing, 2015
The social foraging behavior of Escherichia coli bacteria has been recently used for solving complex realworld search and optimization problems. Bacterial foraging optimization algorithm (BFOA) is an important global optimization method inspired from this behavior. In this paper, a novel method called chemotaxis differential evolution optimization algorithm (CDEOA), which augments BFOA with conditional introduction of differential evolution (DE) and Random Search operators, is proposed. Introduction of these operators is done considering the number of successful run and unsuccessful tumble steps of bacteria. CDEOA was compared with the classical BFOA, two variants of BFOA which use DE operators [Adaptive Chemotactic Bacterial Swarm Foraging Optimization with Differential Evolution Strategy (ACBSFO_DES)], chemotaxis differential evolution (CDE), and the classical DE on all 30 numerical functions of the 2014 Congress on Evolutionary Computation (CEC 2014) Special Session and Competition on Single Objective Real Parameter Numerical Optimization suite. CDEOA was also compared with four state-of-the-art DE variants that competed in CEC 2014. Statistics of the computer simulations over this benchmark suite indicate that CDEOA outperforms, or is comparable to, its competitors in terms of the quality of Communicated by E. Lughofer.
The Adaptive Chemotactic Foraging with Differential Evolution algorithm
2013 World Congress on Nature and Biologically Inspired Computing, 2013
This work proposes the application of a novel evolutionary approach called the Adaptive Chemotactic Foraging with Differential Evolution algorithm (ACF_DE) on benchmark problems. This method is based on the well-known Bacterial Foraging Optimization Algorithm (BFOA), applying appropriate Differential Evolution operators and including an adaptation scheme of the chemotaxis step size to concentrate the search in the desired optimal zone. The hybrid system is compared with those of related methods on benchmark problems showing its high performance in overcoming slow and premature convergence.
A micro-bacterial foraging algorithm for high-dimensional optimization
2009
Abstract Very recently bacterial foraging has emerged as a powerful technique for solving optimization problems. In this paper, we introduce a micro-bacterial foraging optimization algorithm, which evolves with a very small population compared to its classical version. In this modified bacterial foraging algorithm, the best bacterium is kept unaltered, whereas the other population members are reinitialized.
Adaptive computational chemotaxis in bacterial foraging algorithm
2008
Abstract Some researchers have illustrated how individual and groups of bacteria forage for nutrients and to model it as a distributed optimization process, which is called the bacterial foraging optimization (BFOA). One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium, which models a trial solution of the optimization problem. In this article, we analyze the chemotactic step of a one dimensional BFOA in the light of the classical gradient descent algorithm (GDA).
Nature Inspired Optimization Technique: Bacterial Foraging Algorithm
In this era of modernization, deregulation and competition the scenario of the optimization techniques is being evolved dramatically towards the functional mimicry of nature. There are many nature inspired optimization algorithms such as PSO, Ant Colony, Genetic Algorithm, Evolutionary Techniques etc. Recently Bacterial foraging Optimization Algorithm has attracted a lot of attention as a high performance optimizer. In 2002, K. M. Passino proposed Bacterial Foraging Optimization Algorithm (BFOA)[l] for distributed optimization and control. One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium that models a trial solution of the optimization problem. The underlying biology behind the foraging strategy of E.coli is emulated in an extraordinary manner and used as a simple optimization algorithm. This paper presents the BFOA for global optimization. Keywords-Bacterial Foraging, global optimization, Chemotaxis, Optimization Algorithm.
A synergy of differential evolution and bacterial foraging optimization for global optimization
2007
Abstract: The social foraging behavior of Escherichia coli bacteria has recently been studied by several researchers to develop a new algorithm for distributed optimization control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, has many features analogous to classical Evolutionary Algorithms (EA). Passino [1] pointed out that the foraging algorithms can be integrated in the framework of evolutionary algorithms.
Adaptive computational chemotaxis in bacterial foraging optimization: an analysis
2009
Abstract In his seminal paper published in 2002, Passino pointed out how individual and groups of bacteria forage for nutrients and how to model it as a distributed optimization process, which he called the bacterial foraging optimization algorithm (BFOA). One of the major driving forces of BFOA is the chemotactic movement of a virtual bacterium that models a trial solution of the optimization problem.
Genetic Algorithm based Bacterial Foraging Approach for Optimization
research.ijcaonline.org
Bacterial foraging optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. BFOA is inspired by the social foraging behavior of Escherichia coli. BFOA has already drawn the attention of researchers because of its efficiency in solving real world optimization problems arising in several application domains. The underlying biology behind the foraging strategy of E.coli is emulated in an extraordinary manner and used as a simple optimization algorithm. This paper proposes a genetic algorithm (GA) based bacterial foraging (BF) algorithms for function optimization. The proposed method using test functions and the performance of the algorithm is studied with an emphasis on mutation, crossover, variation of step sizes, chemotactic steps, and the lifetime of the bacteria.