Self-Organizing Migrating Algorithm Pareto

Abstract

In this paper, we propose a new method named Pareto-based self-organizing migrating algorithm (SOMA Pareto), in which the algorithm is divided into the Organization, Migration, and Update processes. The important key in the Organization process is the application of the Pareto Principle to select the Migrant and the Leader, increasing the performance of the algorithm. The adaptive PRT, Step, and PRTVector parameters are applied to enhance the ability to search for promising subspaces and then to focus on exploiting that subspaces. Based on the testing results on the well-known benchmark suites such as CEC'13, CEC'15, and CEC'17, the superior performance of the proposed algorithm compared to the SOMA family and the state-of-the-art algorithms such as variant DE and PSO are confirmed. These results demonstrate that SOMA Pareto is an effective, promising algorithm.

Self-organizing migrating algorithm with clustering-aided migration and adaptive perturbation vector control

“…Originally, the 𝑝𝑟𝑡 is one of the user-defined parameters of SOMA. The proposed SOMA variant employs the similar adaptivity of the 𝑝𝑟𝑡 parameter as in other modern variants of SOMA [3,4]. This adaptation affects the covered area by the exploration phase over the algorithm execution.…”

“…Therefore, at the beginning of the algorithm, the exploration phase covers wider hyperspace of solutions between active individual and leader, and this mapping later becomes more focused on the "direct" path between them. The equation of the adaptive 𝑝𝑟𝑡 is defined as (3).…”

SOMA-CLP for competition on bound constrained single objective numerical optimization benchmark

“…An individual called "leader" is selected randomly from the population set of NP individuals for each migration of active individual x. The migration process is defined by the equation (1).…”

“…The main difference between SOMA (and SOMA-CL) and the proposed SOMA-CLP is the usage of the adaptive 𝑝𝑟𝑡 parameter similar as in other modern variants of SOMA [1,2]. This adaptation, as in equation ( 3), affects the covered area by the exploration phase throughout the algorithm lifespan.…”

“…and later popularized in 2001, 2004 and mainly in 2019, when several new powerful versions [1,2] have been introduced for the 100-digit challenge [6]. The workflow of the new proposed SOMA-CLP is given in the next section.…”