Application of nature inspired algorithms in power delivery network design: An industrial case study
01 Oct 2014-pp 103-106
TL;DR: Three natural computing algorithms - Particle Swarm Optimization, Cuckoo Search and Firefly Algorithm are used and compared for solving an industrial problem of power delivery network design.
Abstract: This paper presents application of nature inspired algorithms for solving an industrial problem of power delivery network design. To achieve the target impedance in a power delivery network, optimum number of decoupling capacitors and their optimum locations on board are found from thousands of available capacitors. S-parameters based data is used for the accurate analysis. Three natural computing algorithms - Particle Swarm Optimization, Cuckoo Search and Firefly Algorithm are used and compared for solving this problem.
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06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
Abstract: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced. The evolution of several paradigms is outlined, and an implementation of one of the paradigms is discussed. Benchmark testing of the paradigm is described, and applications, including nonlinear function optimization and neural network training, are proposed. The relationships between particle swarm optimization and both artificial life and genetic algorithms are described.
35,104 citations
"Application of nature inspired algo..." refers background in this paper
...Particle Swarm Optimization (PSO) was introduced by Kennedy and Eberhart (1995) [11][12]....
[...]
01 Dec 2009
TL;DR: A new meta-heuristic algorithm, called Cuckoo Search (CS), is formulated, based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Lévy flight behaviour ofSome birds and fruit flies, for solving optimization problems.
Abstract: In this paper, we intend to formulate a new meta-heuristic algorithm, called Cuckoo Search (CS), for solving optimization problems. This algorithm is based on the obligate brood parasitic behaviour of some cuckoo species in combination with the Levy flight behaviour of some birds and fruit flies. We validate the proposed algorithm against test functions and then compare its performance with those of genetic algorithms and particle swarm optimization. Finally, we discuss the implication of the results and suggestion for further research.
5,521 citations
Book•
01 Feb 2008
TL;DR: This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms.
Abstract: Modern metaheuristic algorithms such as bee algorithms and harmony search start to demonstrate their power in dealing with tough optimization problems and even NP-hard problems. This book reviews and introduces the state-of-the-art nature-inspired metaheuristic algorithms in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search, and firefly algorithms. We also briefly introduce the photosynthetic algorithm, the enzyme algorithm, and Tabu search. Worked examples with implementation have been used to show how each algorithm works. This book is thus an ideal textbook for an undergraduate and/or graduate course. As some of the algorithms such as the harmony search and firefly algorithms are at the forefront of current research, this book can also serve as a reference book for researchers.
3,626 citations
"Application of nature inspired algo..." refers background in this paper
...The inspiration often comes from nature, especially biological systems [9][10]....
[...]
26 Oct 2009
TL;DR: In this article, a new Firefly Algorithm (FA) was proposed for multimodal optimization applications. And the proposed FA was compared with other metaheuristic algorithms such as particle swarm optimization (PSO).
Abstract: 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.
3,436 citations
13 Apr 1997
TL;DR: The paper introduces the algorithm, begins to develop a social science context for it, and explores some aspects of its functioning.
Abstract: Particle swarm adaptation is an optimization paradigm that simulates the ability of human societies to process knowledge The algorithm models the exploration of a problem space by a population of individuals; individuals' successes influence their searches and those of their peers The algorithm is relevant to cognition, in particular the representation of schematic knowledge in neural networks Particle swarm optimization successfully optimizes network weights, simulating the adaptive sharing of representations among social collaborators The paper introduces the algorithm, begins to develop a social science context for it, and explores some aspects of its functioning
1,630 citations
"Application of nature inspired algo..." refers background in this paper
...Particle Swarm Optimization (PSO) was introduced by Kennedy and Eberhart (1995) [11][12]....
[...]