Committee login






Small thumbnail

Reliability Investigation of LED Devices for Public Light Applications

Durability, Robustness and Reliability of Photonic Devices Set

Small thumbnail

Aerospace Actuators 2

Signal-by-Wire and Power-by-Wire

Small thumbnail

Flash Memory Integration

Performance and Energy Considerations

Small thumbnail

Mechanics of Aeronautical Solids, Materials and Structures

Small thumbnail

Engineering Investment Process

Making Value Creation Repeatable

Small thumbnail

Space Strategy

Small thumbnail

Distributed Systems

Concurrency and Consistency

Small thumbnail

Fatigue of Textile and Short Fiber Reinforced Composites

Durability and Ageing of Organic Composite Materials Set – Volume 1

Small thumbnail

Management of the Effects of Coastal Storms

Policy, Scientific and Historical Perspectives

Small thumbnail

Computational Color Science

Variational Retinex-like Methods

Small thumbnail

Guided Randomness in Optimization

Metaheuristics Set – Volume 1

Maurice Clerc, Consultant

ISBN: 9781848218055

Publication Date: May 2015   Hardback   316 pp.

135.00 USD

Add to cart




Optimization metaheuristics proceed to “pot luck” as to whether to carry out certain choices or apply certain rules, for which they must use one or several random number generators (RNGs).
There are several types of RNG, from the truly random to the simply coded. They can be manipulated to produce specific distributions.The performances of an algorithm depend on the RNG used.
This book concerns the comparison of optimizers, it defines an effort–result approach where all classical (median, average, etc.) and some more sophisticated criteria can be derived.
The source codes used for examples are also presented, which enables a reflection of the “unnecessary randomness”, succinctly explaining why and how the stochastic aspect of the optimization could be avoided in certain cases.


Part 1. Randomness in Optimization
1. Necessary Risk.
2. Random Number Generators (RNGS).
3. The Effects of Randomness.
Part 2. Optimizer Comparison
4. Algorithms and Optimizers.
5. Performance Criteria.
6. Comparing Optimizers.
Part 3. Appendices
7. Mathematical Notions.
8. Biases and Signatures.
9. A Pseudo-Scientific Article.
10. Common Mistakes.
11. Unnecessary Randomness? List-based Optimizers.
12. Problems.
13. Source Codes.

About the Authors

Maurice Clerc is a world-renowned specialist in particle swarm optimization. His research and consulting activities concern the resolution of optimization problems.


DownloadTable of Contents - PDF File - 50 Kb

Related Titles

0.02329 s.