Multifidelity approximate Bayesian computation (MF-ABC) is a likelihood-free technique for parameter inference that exploits model approximations to significantly increase the speed of ABC algorithms [T. P. Prescott and R. E. Baker, SIAM/ASA J. Uncertain. Quantif., 8 (2020), pp. 114--138]. Previous work has considered MF-ABC only in context rejection sampling, which does not explore space parti...

This paper presents a new optimization algorithm to design an optimal proportional, integral, derivative (PID) controller in hydro-turbine generator governor for damping output frequency oscillations. In this research, we utilize a stochastic and optimal based PID controller to control frequency-response of the hydro turbine. The proposed algorithm is employed to design an optimal PID controlle...

Artificial Bee Colony (ABC) Algorithm is an optimization algorithm used to find out the global optima. In ABC, each bee stores the information of feasible solution or candidate solution and stochastically modifies this over time, based on the information provided by neighboring bees, it speculative modifies over time and based on the best solution found by the bee itself. . In this proposed wor...

A Modified Artificial Bee Colony (ABC) algorithm for Economic Dispatch (ED) problem has been proposed. The Artificial Bee Colony (ABC) algorithm which is inspired by the foraging behavior of honey bee swarm gives a solution procedure for solving economic dispatch problem. It provides solution more effective than Genetic Algorithm (GA), Particle Swarm Optimization (PSO) and Ant Colony Optimizati...

Artificial Bee Colony (ABC) algorithm is a relatively new swarm intelligence algorithm that has attracted great deal of attention from researchers in recent years with the advantage of less control parameters and strong global optimization ability. However, there is still an insufficiency in ABC regarding its solution search equation, which is good at exploration but poor at exploitation. This ...

Artificial bee colony (ABC) algorithm has proved its importance in solving a number of problems including engineering optimization problems. ABC algorithm is one of the most popular and youngest member of the family of population based nature inspired meta-heuristic swarm intelligence method. ABC has been proved its superiority over some other Nature Inspired Algorithms (NIA) when applied for b...

Artificial Bee Colony (ABC) optimization algorithm is a swarm intelligence based nature inspired algorithm, which has been proved a competitive algorithm with some popular natureinspired algorithms. It is found that ABC is more efficient in exploration as compare to exploitation. With a motivation to balance exploration and exploitation capabilities of ABC, this paper presents an adaptive versi...

The Artificial Bee Colony algorithm is an optimization algorithm than can be applied on the wide range of engineering problems. In this work, the standard ABC algorithm is extended by incorporating a cooperation approach and an algorithm called Split ABC (S-ABC) is developed. SABC aims at improving the performance of the standard ABC algorithm using benefits of cooperation as a social behaviour...

In recent years large number of algorithms based on the swarm intelligence has been proposed by various researchers. The Artificial Bee Colony (ABC) algorithm is one of most popular stochastic, swarm based algorithm proposed by Karaboga in 2005 inspired from the foraging behavior of honey bees. In short span of time, ABC algorithm has gain wide popularity among researchers due to its simplicity...

Particle swarm optimization (PSO) and artificial bee colony (ABC) are new optimization methods that have attracted increasing research interests because of its simplicity and efficiency. However, when being applied to high-dimensional optimization problems, PSO algorithm may be trapped in the local optimal because of its low global exploration efficiency; ABC algorithm has slower convergence sp...