3,223 research outputs found
Improvements in meta-heuristic algorithms for minimum cost design of reinforced concrete rectangular sections under compression and biaxial bending
A numerical procedure is proposed in this paper for achieving the minimum cost design of reinforced concrete rectangular sections under compression and biaxial bending by using biologically-inspired meta-heuristic optimization algorithms. The problem formulation includes the costs of concrete, reinforcement and formwork, obtaining the detailed optimum design in which the section dimensions and the reinforcement correspond to values used in practice. The formulation has been simplified in order to reduce the computational cost while ensuring the rigor necessary to achieve safe designs. The numerical procedure includes the possibility of using high-strength concrete and several design constraints, such as mĂnimum reinforcement and limiting the neutral axis depth. Two numerical examples are presented, drawing comparisons between the results obtained by ACI318 and EC2 standards
Firefly Algorithms for Multimodal Optimization
Nature-inspired algorithms are among the most powerful algorithms for
optimization. This paper intends to provide a detailed description of a new
Firefly Algorithm (FA) for multimodal optimization applications. We will
compare the proposed firefly algorithm with other metaheuristic algorithms such
as particle swarm optimization (PSO). Simulations and results indicate that the
proposed firefly algorithm is superior to existing metaheuristic algorithms.
Finally we will discuss its applications and implications for further research
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
Efficiency Analysis of Swarm Intelligence and Randomization Techniques
Swarm intelligence has becoming a powerful technique in solving design and
scheduling tasks. Metaheuristic algorithms are an integrated part of this
paradigm, and particle swarm optimization is often viewed as an important
landmark. The outstanding performance and efficiency of swarm-based algorithms
inspired many new developments, though mathematical understanding of
metaheuristics remains partly a mystery. In contrast to the classic
deterministic algorithms, metaheuristics such as PSO always use some form of
randomness, and such randomization now employs various techniques. This paper
intends to review and analyze some of the convergence and efficiency associated
with metaheuristics such as firefly algorithm, random walks, and L\'evy
flights. We will discuss how these techniques are used and their implications
for further research.Comment: 10 pages. arXiv admin note: substantial text overlap with
arXiv:1212.0220, arXiv:1208.0527, arXiv:1003.146
An Improved Imperialist Competitive Algorithm based on a new assimilation strategy
Meta-heuristic algorithms inspired by the natural processes are part of the optimization algorithms that they have been considered in recent years, such as genetic algorithm, particle swarm optimization, ant colony optimization, Firefly algorithm. Recently, a new kind of evolutionary algorithm has been proposed that it is inspired by the human sociopolitical evolution process. This new algorithm has been called Imperialist Competitive Algorithm (ICA). The ICA is a population-based algorithm where the populations are represented by countries that are classified as colonies or imperialists. This paper is going to present a modified ICA with considerable accuracy, referred to here as ICA2. The ICA2 is tested with six well-known benchmark functions. Results show high accuracy and avoidance of local optimum traps to reach the minimum global optimal.Three important policies are in the ICA, and assimilation policy is the most important of them. This research focuses on an assimilation policy in the ICA to propose a meta-heuristic optimization algorithm for optimizing function with high accuracy and avoiding to trap in local optima rather than using original ICA by a new assimilation strategy
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