Stopping Criterion Impact on Pure Random Search Optimisation for Intelligent Device Distribution

2010 ◽  
Author(s):  
Michael P. Poland ◽  
Chris D. Nugent ◽  
Hui Wang ◽  
Liming Chen
Keyword(s):  
Random Search ◽  
Stopping Criterion ◽  
Pure Random Search

Approximate Implementations of Pure Random Search in the Presence of Noise

2005 ◽  
Vol 31 (4) ◽  
pp. 601-612 ◽  
Author(s):  
David L. J. Alexander ◽  
David W. Bulger ◽  
James M. Calvin ◽  
H. Edwin. Romeijn ◽  
Ryan L. Sherriff
Keyword(s):  
Random Search ◽  
Pure Random Search

Genetic algorithm and pure random search for exosensor distribution optimisation

2012 ◽  
Vol 4 (6) ◽  
pp. 359 ◽  
Author(s):  
Michael P. Poland ◽  
Christopher D. Nugent ◽  
Hui Wang ◽  
Liming Chen
Keyword(s):  
Genetic Algorithm ◽  
Random Search ◽  
Pure Random Search

Pure Random Search with exponential rate of convergency

Optimization ◽  
2010 ◽  
Vol 59 (2) ◽  
pp. 289-303 ◽  
Author(s):  
Dragan Radulović
Keyword(s):  
Random Search ◽  
Exponential Rate ◽  
Pure Random Search

scholarly journals Pure Random Orthogonal Search (PROS): A Plain and Elegant Parameterless Algorithm for Global Optimization

2021 ◽  
Vol 11 (11) ◽  
pp. 5053
Author(s):  
Vagelis Plevris ◽  
Nikolaos P. Bakas ◽  
German Solorzano
Keyword(s):  
Convergence Rates ◽  
Random Search ◽  
Optimal Solution ◽  
Decision Variable ◽  
Candidate Solution ◽  
Simple Alternative ◽  
Orthogonal Search ◽  
Optimization Search ◽  
Pure Random Search ◽  
Metaheuristic Optimization Algorithms

A new, fast, elegant, and simple stochastic optimization search method is proposed, which exhibits surprisingly good performance and robustness considering its simplicity. We name the algorithm pure random orthogonal search (PROS). The method does not use any assumptions, does not have any parameters to adjust, and uses basic calculations to evolve a single candidate solution. The idea is that a single decision variable is randomly changed at every iteration and the candidate solution is updated only when an improvement is observed; therefore, moving orthogonally towards the optimal solution. Due to its simplicity, PROS can be easily implemented with basic programming skills and any non-expert in optimization can use it to solve problems and start exploring the fascinating optimization world. In the present work, PROS is explained in detail and is used to optimize 12 multi-dimensional test functions with various levels of complexity. The performance is compared with the pure random search strategy and other three well-established algorithms: genetic algorithms (GA), particle swarm optimization (PSO), and differential evolution (DE). The results indicate that, despite its simplicity, the proposed PROS method exhibits very good performance with fast convergence rates and quick execution time. The method can serve as a simple alternative to established and more complex optimizers. Additionally, it could also be used as a benchmark for other metaheuristic optimization algorithms as one of the simplest, yet powerful, optimizers. The algorithm is provided with its full source code in MATLAB for anybody interested to use, test or explore.

scholarly journals On a Parallelised Diffusion Induced Stochastic Algorithm with Pure Random Search Steps for Global Optimisation

Mathematics ◽  
2021 ◽  
Vol 9 (23) ◽  
pp. 3043
Author(s):  
Manuel L. Esquível ◽  
Nadezhda P. Krasii ◽  
Pedro P. Mota ◽  
Nélio Machado
Keyword(s):  
Maximum Principle ◽  
Diffusion Process ◽  
Random Search ◽  
Regular Function ◽  
Local Extremum ◽  
Global Optimisation ◽  
Stochastic Algorithm ◽  
Regular Boundary ◽  
Bounded Set ◽  
Pure Random Search

We propose a stochastic algorithm for global optimisation of a regular function, possibly unbounded, defined on a bounded set with regular boundary; a function that attains its extremum in the boundary of its domain of definition. The algorithm is determined by a diffusion process that is associated with the function by means of a strictly elliptic operator that ensures an adequate maximum principle. In order to preclude the algorithm to be trapped in a local extremum, we add a pure random search step to the algorithm. We show that an adequate procedure of parallelisation of the algorithm can increase the rate of convergence, thus superseding the main drawback of the addition of the pure random search step.

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