A hybrid of bacterial foraging and differential evolution -based distance of sequences

AbstractIn a previous work we presented a new distance that we called the sigma gram distance, which is used to compute the similarity between two sequences. This distance is based on parameters which we computed through an optimization process that used the artificial bee colony; a bio-inspired optimization algorithm. In this paper we show how a hybrid of two optimization algorithms; bacterial foraging and differential evolution, when used to compute the parameters of the sigma gram distance, can yield better results than those obtained by applying artificial bee colony. This superiority in performance is validated through experiments on the same data sets to which artificial bee colony, on the same optimization problem, was tested

Introductory Review of Swarm Intelligence Techniques

With the rapid upliftment of technology, there has emerged a dire need to fine-tune or optimize certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods of optimization through experimentation or simulation, for their generic problem-solving abilities and promising efficacy with the least human intervention. In recent times, the inducement of natural phenomena into algorithm design has immensely triggered the efficiency of optimization process for even complex multi-dimensional, non-continuous, non-differentiable and noisy problem search spaces. This chapter deals with the Swarm intelligence (SI) based algorithms or Swarm Optimization Algorithms, which are a subset of the greater Nature Inspired Optimization Algorithms (NIOAs). Swarm intelligence involves the collective study of individuals and their mutual interactions leading to intelligent behavior of the swarm. The chapter presents various population-based SI algorithms, their fundamental structures along with their mathematical models.Comment: Submitted to Springe

Hybrid spiral-dynamic bacteria-chemotaxis algorithm with application to control two-wheeled machines

This paper presents the implementation of the hybrid spiral-dynamic bacteria-chemotaxis (HSDBC) approach to control two different configurations of a two-wheeled vehicle. The HSDBC is a combination of bacterial chemotaxis used in bacterial forging algorithm (BFA) and the spiral-dynamic algorithm (SDA). BFA provides a good exploration strategy due to the chemotaxis approach. However, it endures an oscillation problem near the end of the search process when using a large step size. Conversely; for a small step size, it affords better exploitation and accuracy with slower convergence. SDA provides better stability when approaching an optimum point and has faster convergence speed. This may cause the search agents to get trapped into local optima which results in low accurate solution. HSDBC exploits the chemotactic strategy of BFA and fitness accuracy and convergence speed of SDA so as to overcome the problems associated with both the SDA and BFA algorithms alone. The HSDBC thus developed is evaluated in optimizing the performance and energy consumption of two highly nonlinear platforms, namely single and double inverted pendulum-like vehicles with an extended rod. Comparative results with BFA and SDA show that the proposed algorithm is able to result in better performance of the highly nonlinear systems

A novel hybrid bacteria-chemotaxis spiral-dynamic algorithm with application to modelling of flexible systems

This paper presents a novel hybrid optimisation algorithm namely HBCSD, which synergises a bacterial foraging algorithm (BFA) and spiral dynamics algorithm (SDA). The main objective of this strategy is to develop an algorithm that is capable to reach a global optimum point at the end of the final solution with a faster convergence speed compared to its predecessor algorithms. The BFA is incorporated into the algorithm to act as a global search or exploration phase. The solutions from the exploration phase then feed into SDA, which acts as a local search or exploitation phase. The proposed algorithm is used in dynamic modelling of two types of flexible systems, namely a flexible robot manipulator and a twin rotor system. The results obtained show that the proposed algorithm outperforms its predecessor algorithms in terms of fitness accuracy, convergence speed, and time-domain and frequency-domain dynamic characterisation of the two flexible systems. © 2014 Elsevier Ltd