An improved evolution based optimization algorithm originated from the concept of SFLA and simulated annealing
01 Dec 2016-pp 193-198
TL;DR: A new approach is proposed based on SFLA and simulatedAnnealing which works on memetic and PSO approaches along with basic temperature idea of simulated annealing and it is shown that it is better than other algorithms in various factors.
Abstract: The paper shows the contribution of evolutionary techniques in the field of optimization. Evolution is a technique which is based on real-world scenarios. It works on Darwin's theory and many algorithms have been proposed in this field to optimize the results. Each algorithm proposed has its advantages and disadvantages and a new technique is brought to overcome the drawbacks of the previously proposed technique. In this paper, a new approach is proposed based on SFLA and simulated annealing which works on memetic and PSO approaches along with basic temperature idea of simulated annealing. The graphs are attached to show the various results obtained. It is shown that it is better than other algorithms in various factors.
Citations
More filters
Posted Content•
TL;DR: A task allocation algorithm that can be decentralised by leveraging the submodularity concepts and sampling process is developed and it is confirmed that the proposed algorithm achieves solution quality, which is comparable to a state-of-the-art algorithm in the monotone case, and much better quality in the non-monot one case with significantly less computational complexity.
Abstract: This paper addresses the task allocation problem for multi-robot systems. The main issue with the task allocation problem is inherent complexity that makes finding an optimal solution within a reasonable time almost impossible. To hand the issue, this paper develops a task allocation algorithm that can be decentralised by leveraging the submodularity concepts and sampling process. The theoretical analysis reveals that the proposed algorithm can provide approximation guarantee of $1/2$ for the monotone submodular case and $1/4$ for the non-monotone submodular case in average sense with polynomial time complexity. To examine the performance of the proposed algorithm and validate the theoretical analysis results, we design a task allocation problem and perform numerical simulations. The simulation results confirm that the proposed algorithm achieves solution quality, which is comparable to a state-of-the-art algorithm in the monotone case, and much better quality in the non-monotone case with significantly less computational complexity.
13 citations
References
More filters
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
"An improved evolution based optimiz..." refers methods in this paper
...Cuckoo Search- It is an optimization algorithm developed by Xin-She Yang and Suash Deb in 2009 [2]....
[...]
Posted Content•
01 Jan 2001
TL;DR: This paper gives a lightning overview of data mining and its relation to statistics, with particular emphasis on tools for the detection of adverse drug reactions.
Abstract: The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.
3,765 citations
"An improved evolution based optimiz..." refers methods in this paper
...The data mining techniques are optimized now-a-days using various optimization techniques [1]....
[...]
Posted Content•
TL;DR: The Bat Algorithm as mentioned in this paper is based on the echolocation behavior of bats and combines the advantages of existing algorithms into the new bat algorithm to solve many tough optimization problems.
Abstract: Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.
3,528 citations
23 Apr 2010
TL;DR: The Bat Algorithm as mentioned in this paper is based on the echolocation behavior of bats and combines the advantages of existing algorithms into the new bat algorithm to solve many tough optimization problems.
Abstract: Metaheuristic algorithms such as particle swarm optimization, firefly algorithm and harmony search are now becoming powerful methods for solving many tough optimization problems. In this paper, we propose a new metaheuristic method, the Bat Algorithm, based on the echolocation behaviour of bats. We also intend to combine the advantages of existing algorithms into the new bat algorithm. After a detailed formulation and explanation of its implementation, we will then compare the proposed algorithm with other existing algorithms, including genetic algorithms and particle swarm optimization. Simulations show that the proposed algorithm seems much superior to other algorithms, and further studies are also discussed.
3,162 citations
TL;DR: Comparisons among the formulation and results of five recent evolutionary-based algorithms: genetic algorithms, memetic algorithms, particle swarm, ant-colony systems, and shuffled frog leaping are compared.
1,268 citations
"An improved evolution based optimiz..." refers background in this paper
...al [16] established comparison of optimization algorithms: GA, memetic algorithms, PSO, ACO, and shuffled frog leaping (SFLA)....
[...]