3,048 research outputs found
Memetic Artificial Bee Colony Algorithm for Large-Scale Global Optimization
Memetic computation (MC) has emerged recently as a new paradigm of efficient
algorithms for solving the hardest optimization problems. On the other hand,
artificial bees colony (ABC) algorithms demonstrate good performances when
solving continuous and combinatorial optimization problems. This study tries to
use these technologies under the same roof. As a result, a memetic ABC (MABC)
algorithm has been developed that is hybridized with two local search
heuristics: the Nelder-Mead algorithm (NMA) and the random walk with direction
exploitation (RWDE). The former is attended more towards exploration, while the
latter more towards exploitation of the search space. The stochastic adaptation
rule was employed in order to control the balancing between exploration and
exploitation. This MABC algorithm was applied to a Special suite on Large Scale
Continuous Global Optimization at the 2012 IEEE Congress on Evolutionary
Computation. The obtained results the MABC are comparable with the results of
DECC-G, DECC-G*, and MLCC.Comment: CONFERENCE: IEEE Congress on Evolutionary Computation, Brisbane,
Australia, 201
Honey Bees Inspired Optimization Method: The Bees Algorithm
Optimization algorithms are search methods where the goal is to find an optimal solution to a problem, in order to satisfy one or more objective functions, possibly subject to a set of constraints. Studies of social animals and social insects have resulted in a number of computational models of swarm intelligence. Within these swarms their collective behavior is usually very complex. The collective behavior of a swarm of social organisms emerges from the behaviors of the individuals of that swarm. Researchers have developed computational optimization methods based on biology such as Genetic Algorithms, Particle Swarm Optimization, and Ant Colony. The aim of this paper is to describe an optimization algorithm called the Bees Algorithm, inspired from the natural foraging behavior of honey bees, to find the optimal solution. The algorithm performs both an exploitative neighborhood search combined with random explorative search. In this paper, after an explanation of the natural foraging behavior of honey bees, the basic Bees Algorithm and its improved versions are described and are implemented in order to optimize several benchmark functions, and the results are compared with those obtained with different optimization algorithms. The results show that the Bees Algorithm offering some advantage over other optimization methods according to the nature of the problem
Artificial Bee Colony Algorithm with Improved Explorations for Numerical Function Optimization
A major problem with Artificial Bee Colony (ABC) algorithm is its
premature convergence to local optima, which originates from lack of explorative
search capability of the algorithm. This paper introduces ABC with Improved
Explorations (ABC-IX), a novel algorithm that modifies both the selection
and perturbation operations of the basic ABC algorithm in an explorative
way. Unlike the basic ABC algorithm, ABC-IX employs a probabilistic, explorative
selection scheme based on simulated annealing which can accept both better
and worse candidate solutions. ABC-IX also maintains a self-adaptive perturbation
rate, separately for each candidate solution, to promote more explorations.
ABC-IX is tested on a number of benchmark problems for numerical optimization
and compared with several recent variants of ABC. Results show that
ABC-IX often outperforms the other ABC-variants on most of the problems
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