A hybrid genetically-bacterial foraging algorithm converged by particle swarm optimisation for global optimisation (original) (raw)
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A hybrid genetic algorithm and bacterial foraging approach for global optimization
2007
The social foraging behavior of Escherichia coli bacteria has been used to solve optimization problems. This paper proposes a hybrid approach involving genetic algorithms (GA) and bacterial foraging (BF) algorithms for function optimization problems. We first illustrate the proposed method using four 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.
A Comprehensive Review on Bacteria Foraging Optimization Technique
Multi-objective Swarm Intelligence, Springer, 2015
Intelligent applications using evolutionary algorithms are becoming famous because of their ability to handle any real time complex and uncertain situations. Swarm intelligence, now-a-days has become a research focus which studies the collective behavior existing among the natural species which lives in group. Bacteria Foraging Optimization (BFO) is an optimization algorithm based on the social intelligence behavior of E.coli bacteria. Literature has witnessed the applications of BFO algorithm and the results reported are promising with regard to its convergence and accuracy. Several studies based on distributed control and optimization also suggested that algorithm based on BFO can be treated as global optimization technique. In this chapter, we have focused on the behavior of biological bacterial colony followed by the optimization algorithm based on bacterial colony foraging. We have also explored variations in the components of BFO algorithm (Revised BFO), hybridization of BFO with other Evolutionary Algorithms (Hybrid BFO) and multi-objective BFO. Finally, we have analyzed some applications of BFO algorithm in various domains.
Adaptive Bacterial Foraging Optimization
Abstract and Applied Analysis, 2011
Bacterial Foraging Optimization BFO is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of E. coli bacteria. Up to now, BFO has been applied successfully to some engineering problems due to its simplicity and ease of implementation. However, BFO possesses a poor convergence behavior over complex optimization problems as compared to other nature-inspired optimization techniques. This paper first analyzes how the run-length unit parameter of BFO controls the exploration of the whole search space and the exploitation of the promising areas. Then it presents a variation on the original BFO, called the adaptive bacterial foraging optimization ABFO , employing the adaptive foraging strategies to improve the performance of the original BFO. This improvement is achieved by enabling the bacterial foraging algorithm to adjust the run-length unit parameter dynamically during algorithm execution in order to balance the exploration/exploitation tradeoff. The experiments compare the performance of two versions of ABFO with the original BFO, the standard particle swarm optimization PSO and a real-coded genetic algorithm GA on four widely-used benchmark functions. The proposed ABFO shows a marked improvement in performance over the original BFO and appears to be comparable with the PSO and GA.
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 Crossover Bacterial Foraging Optimization Algorithm
This paper presents a modified bacterial foraging optimization algorithm called crossover bacterial foraging optimization algorithm, which inherits the crossover technique of genetic algorithm. This can be used for improvising the evaluation of optimal objective function values. The idea of using crossovermechanism is to search nearby locations by offspring (50 percent of bacteria), because they are randomly produced at different locations. In the traditional bacterial foraging optimization algorithm, search starts from the same locations (50 percent of bacteria are replicated) which is not desirable. Seven different benchmark functions are considered for performance evaluation. Also, comparison with the results of previous methods is presented to reveal the effectiveness of the proposed algorithm.
Bacterial foraging optimization algorithm: theoretical foundations, analysis, and applications
2009
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.
In this paper, a smart bacterial foraging optimization method considering evolutionary and heuristic strategies (SBFEH) is developed, which enjoys an individual intelligence (how to achieve a goal) and social intelligence (how to collaborate or compete with others for resources). The algorithm inspires some evolutionary and physiological heuristic strategies, which provide a higher performance in terms of efficiency and effectiveness. In other words, the exploration and exploitation of the proposed algorithm are improved. The proposed algorithm is applied on different benchmark functions including some CEC05/13 functions and two well-known engineering design optimization problems, the results obtained show that the SBFEHS algorithm is either more accurate and scalable or competitive than those algorithms available in the literature. In addition, a practical water management problem-based optimization is deal with the proposed algorithm in comparison with other state of art solutions.
Simplifying the Bacteria Foraging Optimization Algorithm
IEEE Congress on Evolutionary Computation, 2010
The Bacterial Foraging Optimization Algorithm is a swarm intelligence technique which models the individual and group foraging policies of the E. Coli bacteria as a distributed optimization process. The algorithm is structurally complex due to its nested loop architecture and includes several parameters whose selection deeply influences the result. This paper presents some modifications to the original algorithm that simplifies the algorithm structure, and the inclusion of best member information into the search strategy, which improves the performance. The results on several benchmarks show reasonable performance in most tests and a considerable improvement in some complex functions. Also, with the use of the T-Test we were able to confirm that the performance enhancement is statistically significant.
Synergy of PSO and bacterial foraging optimization—a comparative study on numerical benchmarks
2007
Social foraging behavior of Escherichia coli bacteria has recently been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA), as it is called now, is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real-world optimization problems.
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.