Designing of rule base for a TSK- fuzzy system using bacterial foraging optimization algorithm (BFOA)

AbstractManual construction of a rule base for a fuzzy system is a hard and time-consuming task that requires expert knowledge. To ameliorate that, researchers have developed some methods that are more based on training data than on expert knowledge to gradually identify the structure of rule bases. In this paper we propose a method based on bacterial foraging optimization algorithm (BFOA), which simulates the foraging behavior of “E.coli” bacterium, to tune Gaussian membership functions parameters of a TSK-fuzzy system rule base. The effectiveness of modified BFOA in such identifications is then revealed for designing a fuzzy control system, via a comparison with available methods

QUORUM SENSING BASED BACTERIAL SWARM OPTIMIZATION ON TEST BENCHMARK FUNCTIONS

The Bacterial swarm optimization is one of the latest optimization technique mainly inspired from the swarm of bacteria. This paper introduces an intelligent Quorum sensing based Bacterial Swarm Optimization (QBSO) technique for testing and validation. The quorum sensing senses the best position of the bacteria by knowing the worst place in search space. By knowing these positions, the best optimal solution is attained. Here in this proposed QBSO algorithm the exploration capability of the bacteria is well improved. The proposed technique is validated on the seven standard benchmark with unimodal and multimodal test function for its feasibility and optimality. The basic swarm based optimization algorithms such as Particle Swarm Optimization, Ant Colony Optimization, Biogeography Based Optimization, Simulated Bee Colony and conventional Bacterial Swarm Optimization with the standard parameters are simulated and associated with the proposed technique. The attained results evidently indicate that the proposed method outperforms from the considered optimization methods. Further, the proposed technique may apply to any engineering problems, especially for complex real time optimization problems

Hybrid bacterial foraging sine cosine algorithm for solving global optimization problems

This paper proposes a new hybrid algorithm between Bacterial Foraging Algorithm (BFA) and Sine Cosine Algorithm (SCA) called Hybrid Bacterial Foraging Sine Cosine Algorithm (HBFSCA) to solve global optimization problems. The proposed HBFSCA algorithm synergizes the strength of BFA to avoid local optima with the adaptive step-size and highly randomized movement in SCA to achieve higher accuracy compared to its original counterparts. The performances of the proposed algorithm have been investigated on a set of single-objective minimization problems consist of 30 benchmark functions, which include unimodal, multimodal, hybrid, and composite functions. The results obtained from the test functions prove that the proposed algorithm outperforms its original counterparts significantly in terms of accuracy, convergence speed, and local optima avoidance