752 publications from this institution
By analyzing the similarity of a self-organizing system and an optimization process, we highlight that optimization can be considered as self-organization. We analyze the characteristics of some popular met heuristic algorithms such as firefly algorithm and cuckoo search for applications in self-organizing systems.
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Swarm intelligence and bio-inspired algorithms form a hot topic in the developments of new algorithms inspired by nature. These nature-inspired metaheuristic algorithms can be based on swarm intelligence, biological systems, physical and chemical systems. Therefore, these algorithms can be called swarm-intelligence-based, bio-inspired, physics-based and chemistry-based, depending on the sources of inspiration. Though not all of them are efficient, a few algorithms have proved to be very effi cient and thus have become popular tools for solving real-world problems. Some algorithms are insuffici ently studied. The purpose of this review is to present a relatively comprehensive list of all the algorithms in the literature, so as to inspire further research.
Swarm intelligence is a very powerful technique appropriate to optimization. In this paper, we present a new swarm intelligence algorithm, which is based on the bat algorithm. Bat algorithm has been hybridized with differential evolution strategies. This hybridization showed very promising results on standard benchmark functions and also significantly improved the original bat algorithm.
No abstract is provided for this article.
Computational optimization is becoming a standard tool that is widely used in engineering design and industrial applications. Products and services are often concerned with the maximization of profits and reduction of cost, but also aim at being more energy-efficient, environment-friendly and safety-ensured; at the same time they are limited by resources, time and money. Despite of increasing computer power and availability of better simulation packages, there are a number of challenges remaining when applying numerical optimization methods for real-world engineering problems. Also, new challenges emerge when attempting to attack problems whose solution by means of simulation-based optimization was not even possible in the past. This third workshop on Computational Optimization, Modelling and Simulation (COMS 2012) at ICCS 2012 will further summarize the latest developments of optimization and modelling and their applications in science, engineering and industry.
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.
No abstract is provided for this article.
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 Lévy flights.We will discuss how these techniques are used and their implications for further research.
The increasing popularity of metaheuristic algorithms has attracted a great deal of attention in algorithm analysis and performance evaluations. No-free-lunch theorems are of both theoretical and practical importance, while many important studies on convergence analysis of various metaheuristic algorithms have proven to be fruitful. This paper discusses the recent results on no-free-lunch theorems and algorithm convergence, as well as their important implications for algorithm development in practice. Free lunches may exist for certain types of problem. In addition, we will highlight some open problems for further research.
Differential evolution is an optimization algorithm with vector-based mutation and crossover. This chapter introduces the principle of differential evolution and its variants. A demo implementation will be provided with discussions.
Metaheuristic algorithms have become powerful tools for modeling and optimization. This chapter provides an overview of nature-inspired metaheuristic algorithms, especially those developed in the last two decades, and their applications. We will briefly introduce algorithms such as genetic algorithms, differential evolution, genetic programming, fuzzy logic, and most importantly, swarm-intelligence-based algorithms such as ant and bee algorithms, particle swarm optimization, cuckoo search, firefly algorithm, bat algorithm, and krill herd algorithm. We also briefly describe the main characteristics of these algorithms and outline some recent applications of these algorithms.KeywordsNature-inspired algorithm; metaheuristic algorithms; modeling; optimization.
