Adaptive Learning Search, a New Tool to Help Comprehending Metaheuristics

Abstract

The majority of the algorithms used to solve hard optimization problems today are population metaheuristics. These methods are often presented under a purely algorithmic angle, while insisting on the metaphors which led to their design. We propose in this article to regard population metaheuristics as methods making evolution a probabilistic sampling of the objective function, either explicitly, implicitly, or directly, via processes of learning, diversification, and intensification. We present a synthesis of some metaheuristics and their functioning seen under this angle, called Adaptive Learning Search. We discuss how to design metaheuristics following this approach, and propose an implementation with our Open Metaheuristics framework, along with concrete examples.

Metaheuristic algorithms for PID controller parameters tuning: review, approaches and open problems

“…Several other taxonomies of metaheuristic algorithms can be seen in Kumar and Bawa (2018); Shaheen et al (2018); DrEO et al (2007); Beheshti and Shamsuddin (2013); Gogna and Tayal (2013); Hussien et al (2020); Kumar and Bawa (2020); Abdel-Basset et al (2018). Fig.…”

Continuous Dynamic Optimization

“…While the algorithm proceeds on a dimension, other dimensions are left apart. The final algorithm is presented in Algorithm 1: Tests (figures 5, 6 and 7) were performed using the Open Metaheuristic project platform (see Dréo et al (2007) and Dréo et al (2005) for more information). We used a set of dynamic continuous test functions that we introduced in Dréo et al (2007).…”

“…The final algorithm is presented in Algorithm 1: Tests (figures 5, 6 and 7) were performed using the Open Metaheuristic project platform (see Dréo et al (2007) and Dréo et al (2005) for more information). We used a set of dynamic continuous test functions that we introduced in Dréo et al (2007). Adding electrostatic charges to the ants has enhanced the found solution on the totality of the dynamic test functions (see tables 1, 2 and 3).…”

pALS: An object-oriented framework for developing parallel cooperative metaheuristics

“…For that reason the framework's development was focused on a series of concepts extracted from the Adaptive Learning Search known as ALS [11] for defining metaheuristics. In the ALS framework, metaheuristics are composed by a series of operators which interact between them through cooperation strategies.…”