scholarly journals A framework for locally convergent random-search algorithms for discrete optimization via simulation

2007 ◽  
Vol 17 (4) ◽  
pp. 19 ◽  
Author(s):  
L. Jeff Hong ◽  
Barry L. Nelson
Keyword(s):  
Discrete Optimization ◽  
Random Search ◽  
Search Algorithms ◽  
Optimization Via Simulation

Solving Machine Loading Problem of FMS

2011 ◽  
pp. 19-43 ◽  
Author(s):  
Anoop Prakash ◽  
Nagesh Shukla ◽  
Ravi Shankar ◽  
Manoj Kumar Tiwari
Keyword(s):  
Genetic Algorithm ◽  
Ant Colony Optimization ◽  
Flexible Manufacturing ◽  
Manufacturing Systems ◽  
Random Search ◽  
Search Algorithms ◽  
Ant Colony ◽  
Complex Nature ◽  
Machine Loading ◽  
Loading Problem

Artificial intelligence (AI) refers to intelligence artificially realized through computation. AI has emerged as one of the promising computer science discipline originated in mid-1950. Over the past few decades, AI based random search algorithms, namely, genetic algorithm, ant colony optimization, and so forth have found their applicability in solving various real-world problems of complex nature. This chapter is mainly concerned with the application of some AI based random search algorithms, namely, genetic algorithm (GA), ant colony optimization (ACO), simulated annealing (SA), artificial immune system (AIS), and tabu search (TS), to solve the machine loading problem in flexible manufacturing system. Performance evaluation of the aforementioned search algorithms have been tested over standard benchmark dataset. In addition, the results obtained from them are compared with the results of some of the best heuristic procedures in the literature. The objectives of the present chapter is to make the readers fully aware about the intricate solutions existing in the machine loading problem of flexible manufacturing systems (FMS) to exemplify the generic procedure of various AI based random search algorithms. Also, the present chapter describes the step-wise implementation of search algorithms over machine loading problem.

A note on continuous search algorithms

1987 ◽  
Vol 24 (1) ◽  
pp. 277-280
Author(s):  
L. E. Garey ◽  
R. D. Gupta
Keyword(s):  
Random Search ◽  
Search Algorithms ◽  
Search Methods ◽  
Average Complexity

Continuous random search methods with an average complexity given by O(log(1/ε)) for ε → 0 where ε is a given accuracy were presented in a recent paper. In this article an example of an O(log log(1/ε)) method is presented and illustrated.

Stopping rules in k-adaptive global random search algorithms

2010 ◽  
Vol 48 (1) ◽  
pp. 87-97 ◽  
Author(s):  
Anatoly Zhigljavsky ◽  
Emily Hamilton
Keyword(s):  
Random Search ◽  
Search Algorithms ◽  
Stopping Rules ◽  
Global Random Search

Incorporating Filters in Random Search Algorithms for the Hourly Operation of a Multireservoir System

2018 ◽  
Vol 144 (2) ◽  
pp. 04017088 ◽  
Author(s):  
Duan Chen ◽  
Arturo S. Leon ◽  
Claudio Fuentes ◽  
Nathan L. Gibson ◽  
Hui Qin
Keyword(s):  
Random Search ◽  
Search Algorithms ◽  
Multireservoir System

scholarly journals Bayesian Optimization for Conformer Generation

2018 ◽  
Author(s):  
Lucian Chan ◽  
Geoffrey Hutchison ◽  
Garrett Morris
Keyword(s):  
Lower Energy ◽  
Random Search ◽  
Search Algorithms ◽  
Stochastic Search ◽  
Search Method ◽  
Low Energy ◽  
Bayesian Optimization ◽  
Bayesian Optimization Algorithm ◽  
Conformer Generation ◽  
Stochastic Search Method

<div><div><div><div><p>Generating low-energy molecular conformers is a key task for many areas of computational chemistry, molecular modeling and cheminformatics. Most current conformer generation methods primarily focus on generating geometrically diverse conformers rather than finding the most probable or energetically lowest minima. Here, we present a new stochastic search method using Bayesian Optimization Algorithm (BOA) for finding the lowest energy conformation of a given molecule. We compare BOA with uniform random search, and systematic search as implemented in Confab, to determine which method finds the lowest energy. Energetic difference, root-mean-square deviation (RMSD), and torsion fingerprint deviation (TFD) are used to quantify differences between the conformer search algorithms. In general, we find BOA requires far fewer evaluations than systematic or uniform random search to find low-energy minima. For molecules with four or more rotatable bonds, Confab typically evaluates 104 (median) conformers in its search, while BOA only requires 102 energy evaluations to find top candidates. Despite evaluating fewer conformers, for many molecules, BOA finds lower-energy conformations than an exhaustive systematic Confab search.</p></div></div></div></div>

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