Improved Modified Bacterial Foraging Optimization Algorithm to Solve Constrained Numerical Optimization Problems (original) (raw)
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Evolutionary bacterial foraging algorithm to solve constraint numerical optimization problems
2016
A version of Modified Bacterial Foraging Optimization Algorithm to solve Constraints Numerical Optimization is tested. The proposal uses mutation operator, skew mechanism and local search operator. To prove the effectiveness of the mechanism and adaptations proposed, 24 well-known test problems are solved along set experiments. Performance measures are used for validating results obtained by the proposal and they are compared against state-of-the-art algorithms. The results show that the proposed algorithm is able to generate feasible solutions within of feasible region with few evaluations and improves them over the generations. Moreover, the results are competitive against the comparison algorithms based on performance measures found in the literature. Bacterial foraging optimization, mutation operator, swarm intelligence, Constrained optimization, premature convergence.
Bacterial foraging optimization algorithm with mutation to solve constrained problems
Acta Universitaria, 2019
A simple version of a Swarm Intelligence algorithm called bacterial foraging optimization algorithm with mutation and dynamic stepsize (BFOAM-DS) is proposed. The bacterial foraging algorithm has the ability to explore and exploit the search space through its chemotactic operator. However, premature convergence is a disadvantage. This proposal uses a mutation operator in a swim, similar to evolutionary algorithms, combined with a dynamic stepsize operator to improve its performance and allows a better balance between the exploration and exploitation of the search space. BFOAM-DS was tested in three well-known engineering design optimization problems. Results were analyzed with basic statistics and common measures for nature-inspired constrained optimization problems to evaluate the behavior of the swim with a mutation operator and the dynamic stepsize operator. Results were compared against a previous version of the proposed algorithm to conclude that BFOAM-DS is competitive and better than a previous version of the algorithm.
This paper presents the addition of an adaptive stepsize value and a local search operator to the modified bacterial foraging algorithm (MBFOA) to solve constrained optimization problems. The adaptive stepsize is used in the chemotactic loop for each bacterium to promote a suitable sampling of solutions and the local search operator aims to promote a better trade-off between exploration and exploitation during the search. Three MBFOA variants, the original one, another with only the adaptive stepsize and a third one with both, the adaptive stepsize and also the local search operator are tested on a set of wellknown benchmark problems. Furthermore, the most competitive variant is compared against some representative nature-inspired algorithms of the state-of-the-art. The results obtained provide evidence on the utility of each added mechanism, while the overall performance of the approach makes it a viable option to solve constrained optimization problems.
International Journal of Computer Applications, 2013
For the solution of a set equation (linear or non-linear) with n number (n > 1) of variables we need at least n number of different relations (called as rank). Our present work is showing how the bio-inspired Bacteria Foraging Optimization Algorithm (BFOA), which is mimicry of the life-cycle of common type of bacteria like E.Coli, can be used to solve such system of equation with rank less than or equal to n. The BFOA simulates efficient nutrient foraging technique called as Chemotaxis to maximize the intake energy per unit time spend, the reproduction for evolution and the eliminationdispersal for environmental changes like any kind of natural calamities that are observed in the Bacterial system. As a sample tests we have used a numbers of system of linear equations with rank equal to the number of variables and a system of non-linear equations used in the derivation process of 4 th order Runge-Kutta method for the ordinary differential equation solution, and experimental results are showing the applicability of the BFOA and in case of Runge-Kutta method we present an alternative form of the recursive equation.
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.
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.
Comparative Analysis of Bacterial Foraging Optimization Algorithm with Simulated Annealing
2014
Bacterial foraging optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest for 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. Simulated annealing (SA) is a method for solving unconstrained and bound-constrained optimization problems. The method models the physical process of heating a material and then slowly lowering the temperature to decrease defects, thus minimizing the system energy. In present paper, a detailed explanation of this algorithm is given. Comparative analysis of BFOA with Simulated Annealing (SA) is presented.
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.
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.
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.