516 research outputs found
A hybrid swarm-based algorithm for single-objective optimization problems involving high-cost analyses
In many technical fields, single-objective optimization procedures in
continuous domains involve expensive numerical simulations. In this context, an
improvement of the Artificial Bee Colony (ABC) algorithm, called the Artificial
super-Bee enhanced Colony (AsBeC), is presented. AsBeC is designed to provide
fast convergence speed, high solution accuracy and robust performance over a
wide range of problems. It implements enhancements of the ABC structure and
hybridizations with interpolation strategies. The latter are inspired by the
quadratic trust region approach for local investigation and by an efficient
global optimizer for separable problems. Each modification and their combined
effects are studied with appropriate metrics on a numerical benchmark, which is
also used for comparing AsBeC with some effective ABC variants and other
derivative-free algorithms. In addition, the presented algorithm is validated
on two recent benchmarks adopted for competitions in international conferences.
Results show remarkable competitiveness and robustness for AsBeC.Comment: 19 pages, 4 figures, Springer Swarm Intelligenc
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
Learning to Control Differential Evolution Operators
Evolutionary algorithms are widely used for optimsation by researchers in academia and industry. These algorithms have parameters, which have proven to highly determine the performance of an algorithm. For many decades, researchers have focused on determining optimal parameter values for an algorithm. Each parameter configuration has a performance value attached to it that is used to determine a good configuration for an algorithm. Parameter values depend on the problem at hand and are known to be set in two ways, by means of offline and online selection. Offline tuning assumes that the performance value of a configuration remains same during all generations in a run whereas online tuning assumes that the performance value varies from one generation to another.
This thesis presents various adaptive approaches each learning from a range of feedback received from the evolutionary algorithm. The contributions demonstrate the benefits of utilising online and offline learning together at different levels for a particular task. Offline selection has been utilised to tune the hyper-parameters of proposed adaptive methods that control the parameters of evolutionary algorithm on-the-fly. All the contributions have been presented to control the mutation strategies of the differential evolution. The first contribution demonstrates an adaptive method that is mapped as markov reward process. It aims to maximise the cumulative future reward. Next chapter unifies various adaptive methods from literature that can be utilised to replicate existing methods and test new ones. The hyper-parameters of methods in first two chapters are tuned by an offline configurator, irace. Last chapter proposes four methods utilising deep reinforcement learning model. To test the applicability of the adaptive approaches presented in the thesis, all methods are compared to various adaptive methods from literature, variants of differential evolution and other state-of-the-art algorithms on various single objective noiseless problems from benchmark set, BBOB
Stability Analysis of Artificial Bee Colony Optimization Algorithm
Theoretical analysis of swarm intelligence and evolutionary algorithms is relatively less explored area of research. Stability and convergence analysis of swarm intelligence and evolutionary algorithms can help the researchers to fine tune the parameter values. This paper presents the stability analysis of a famous Artificial Bee Colony (ABC) optimization algorithm using von Neumann stability criterion for two-level finite difference scheme. Parameter selection for the ABC algorithm is recommended based on the obtained stability conditions. The findings are also validated through numerical experiments on test problems
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
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