loading
Papers Papers/2022 Papers Papers/2022

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Mohiul Islam ; Nawwaf Kharma ; Vaibhav Sultan ; Xiaojing Yang ; Mohamed Mohamed and Kalpesh Sultan

Affiliation: Department of Electrical & Computer Engineering, Concordia University, Montreal and Canada

Keyword(s): Evolutionary Computation, Evolutionary Algorithms, Genetic Algorithms, Simulated Annealing, Particle Swarm Optimization, Covariance Matrix Adaptation Evolution Strategy, Multi-dimension Optimization, Multi-modal Optimization, Parallel Scalable Optimization.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Computational Intelligence ; Evolutionary Computing ; Genetic Algorithms ; Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Soft Computing

Abstract: Map, Explore & Exploit (ME2) is a scalable meta-heuristic for problems in the field of multi-modal, multi-dimension optimization. It has a modular design with three phases, as reflected by its name. Its first phase (Map) generates a set of samples that is mostly uniformly distributed over the search space. The second phase (Explore) explores the neighbourhood of each sample point using an evolutionary strategy, to find a good - not necessarily optimal - set of neighbours. The third phase (Exploit) optimizes the results of the second phase. This final phase applies a simple gradient descent algorithm to find the local optima for each and all of the neighbourhoods, with the objective of finding a/the global optima of the whole space. The performance of ME2 is compared, on a fair basis, with the performance of benchmark optimization algorithms: Genetic Algorithms, Particle Swarm Optimization, Simulated Annealing and Covariance Matrix Adaptation Evolution Strategy. In most test cases it finds the global optima earlier than the other algorithms. It also scales-up, without loss of performance, to higher dimensions. Due to the distributed nature of ME2’s second and third phase, it can be comprehensively parallelized. The search & optimization process during these two phases can be applied to each sample point independently of all the others. A multi-threaded version of ME2 was written and compared to its single-threaded version, resulting in a near-linear speed-up as a function of the number of cores employed. (More)

CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 54.197.64.207

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

Paper citation in several formats:
Islam, M.; Kharma, N.; Sultan, V.; Yang, X.; Mohamed, M. and Sultan, K. (2019). ME2: A Scalable Modular Meta-heuristic for Multi-modal Multi-dimension Optimization. In Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - ECTA; ISBN 978-989-758-384-1; ISSN 2184-3236, SciTePress, pages 196-204. DOI: 10.5220/0008169001960204

@conference{ecta19,
author={Mohiul Islam. and Nawwaf Kharma. and Vaibhav Sultan. and Xiaojing Yang. and Mohamed Mohamed. and Kalpesh Sultan.},
title={ME2: A Scalable Modular Meta-heuristic for Multi-modal Multi-dimension Optimization},
booktitle={Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - ECTA},
year={2019},
pages={196-204},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008169001960204},
isbn={978-989-758-384-1},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Computational Intelligence (IJCCI 2019) - ECTA
TI - ME2: A Scalable Modular Meta-heuristic for Multi-modal Multi-dimension Optimization
SN - 978-989-758-384-1
IS - 2184-3236
AU - Islam, M.
AU - Kharma, N.
AU - Sultan, V.
AU - Yang, X.
AU - Mohamed, M.
AU - Sultan, K.
PY - 2019
SP - 196
EP - 204
DO - 10.5220/0008169001960204
PB - SciTePress