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AMLP-RS-CMSA-ESII

July 2022

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CC BY-NC-SA 4.0

Languages: MATLAB

Authors:

UNSW Sydney

Saber M. Elsayed

UNSW Sydney

UNSW Sydney

Australian Defence Force Academy

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This is the MATLAB code of adaptive multilevel prediction method integrated with an improved covariance matrix self-adaptation evolution strategy with repelling subpopulations (AMLP-RS-CMSA-ESII). Please check the readme.pdf file for the instructions and license before use.

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amlp_rs_cmsa_esii_code_and_repo

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Code

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Principal Component Analysis (PCA)

October 1980

Alaa Tharwat

This file includes a tutorial paper and presentation about PCA. Moreover, matlab code for some experiments using PCA and a tutorial code for the PCA using Covariance matrix and SVD methods.

Technical Report

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Benchmarking AMLP-RS-CMSA-ESII for the CEC'2022 Competition on Seeking Multiple Optima in Dynamic En...

August 2022

This report presents the results of a dynamic multimodal optimization method formed by the integration of the adaptive multilevel prediction (AMLP) method with the recent variant of co-variance matrix self-adaptation evolution strategy with repelling subpopulation (RS-CMSAESII) on the test problems of the CEC'2022 competition on seeking multiple optima in dynamic environments.

Technical Report

Full-text available

Benchmarking an Improved RS-CMSA-ES for WCCI/CEC'2020 Competition on Niching Methods for Multimodal...

August 2021

This report presents results of benchmarking an improved version RS-CMSA-ES on the test suite employed for WCCI/CEC'2020 competition on niching methods for multimodal optimization. An upper-level description of the improvement to the existing RS-CMSA-ES code is provided.

Article

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Static and Dynamic Multimodal Optimization by Improved Covariance Matrix Self-Adaptation Evolution S...

October 2021 · IEEE Transactions on Evolutionary Computation

The covariance matrix self-adaptation evolution strategy with repelling subpopulations (RS-CMSA-ES) is one of the most successful multimodal optimization methods currently available. However, some of its components may become inefficient in certain situations. This study introduces the second variant of this method, called RS-CMSA-ESII. It improves the adaptation schemes for the normalized taboo ... [Show full abstract] distances of the archived solutions and the covariance matrix of the subpopulation, the termination criteria for the subpopulations, and the way in which the infeasible solutions are treated. It also improves the time complexity of RS-CMSA-ES by updating the initialization procedure of a subpopulation and developing a more accurate metric for determining critical taboo regions. The effects of these modifications are illustrated by designing controlled numerical simulations. RS-CMSA-ESII is then compared with the most successful and recent niching methods for multimodal optimization on a widely adopted test suite. The results obtained reveal the superiority of RS-CMSA-ESII over these methods, including the winners of the competition on niching methods for multimodal optimization in previous years. Besides, this study extends RS-CMSA-ESII to dynamic multimodal optimization and compares it with a few recently proposed methods on the modified moving peak benchmark functions.

Article

Full-text available

A Novel Parametric Benchmark Generator for Dynamic Multimodal Optimization

June 2021 · Swarm and Evolutionary Computation

In most existing studies on dynamic multimodal optimization (DMMO), numerical simulations have been performed using the Moving Peaks Benchmark (MPB), which is a two-decade-old test suite that cannot simulate some critical aspects of DMMO problems. This study proposes the Deterministic Distortion and Rotation Benchmark (DDRB), a method to generate deterministic DMMO test problems that can simulate ... [Show full abstract] more diverse types of challenges when compared to existing benchmark generators for DMMO. DDRB allows for controlling the intensity of each type of challenge independently, enabling the user to pinpoint the pros and cons of a DMMO method. DDRB first develops an existing approach for generation of static multimodal functions in which the difficulty of global optimization can be controlled. Then, it proposes a scaling function to dynamically change the relative distribution, shapes, and sizes of the basins. A deterministic technique to control the regularity of the pattern in the change is also proposed. Using these components, a parametric test suite consisting of ten test problems is developed for DMMO. Mean Robust Peak Ratio for measuring the performance of DMMO methods is formulated to overcome the sensitivity of the conventional peak ratio indicator to the predefined threshold and niche radius. Numerical results of a successful multimodal optimization method, when augmented with a simple strategy to utilize previous information, are provided on the proposed test problems in different scenarios with the aim of serving as a reference for future studies.