Chemo-inspired genetic algorithm for function optimization (original) (raw)
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A Novel Hybrid Genetic Algorithm for Unconstrained and Constrained Function Optimization
Bio-Inspired Computing for Information Retrieval Applications, 2000
During the past decade, academic and industrial communities are highly interested in evolutionary techniques for solving optimization problems. Genetic Algorithm (GA) has proved its robustness in solving all most all types of optimization problems. To improve the performance of GA, several modifications have already been done within GA. Recently GA has been hybridized with many other nature-inspired algorithms. As such Bacterial Foraging Optimization (BFO) is popular bio inspired algorithm based on the foraging behavior of E. coli bacteria. Many researchers took active interest in hybridizing GA with BFO. Motivated by such popular hybridization of GA, an attempt has been made in this chapter to hybridize GA with BFO in a novel fashion. The Chemo-taxis step of BFO plays a major role in BFO. So an attempt has been made to hybridize Chemo-tactic step with GA cycle and the algorithm is named as Chemo-inspired Genetic Algorithm (CGA). It has been applied on benchmark functions and real life application problem to prove its efficacy.
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.
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.
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.
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.
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.
Applied Mathematics & Information Sciences, 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.
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 Novel Bio-Inspired Optimization Algorithm
2013
— In this paper, a new meta-heuristic bio-inspired optimization algorithm, called Cuttlefish Algorithm (CFA) is presented. The algorithm mimics the mechanism of color changing behavior used by the cuttlefish to solve numerical global optimization problems. The patterns and colors seen in cuttlefish are produced by reflected light from different layers of cells including (chromatophores, leucophores and iridophores) stacked together, and it is the combination of certain cells at once that allows cuttlefish to possess such a large array of patterns and colors. The proposed algorithm considers two main processes: reflection and visibility. Reflection process is proposed to simulate the light reflection mechanism used by these three layers, while the visibility is proposed to simulate the visibility of matching pattern used by the cuttlefish. These two processes are used as a search strategy to find the global optimal solution. Efficiency of this algorithm is also tested with some other popular biology inspired optimization algorithms such as Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and Bees Algorithm (BA) that have been previously proposed in the literature. Simulations and obtained results indicate that the proposed CFA is superior to other algorithms.