A Modified EACOP Implementation for Real-Parameter Single Objective Optimization Problems

Andrea Tangherloni, Vasco Coelho, Francesca M. Buffa, Paolo Cazzaniga

Abstract:

Evolutionary algorithms are effective techniques for
optimizing non-linear and complex high-dimensional problems.
However, most of them require a precise fine-tuning of their
functioning settings to achieve satisfactory results. In this work,
we propose a modified version of an evolutionary approach
called the Evolutionary Algorithm for COmplex-process oPtimization (EACOP), designed to have a limited number of hyperparameters. The base version of EACOP (bEACOP) combines
different strategies, including the scatter search methodology,
local searches, and a novel combination method based on path
relinking to balance the exploration and exploitation phases.
Our improved version (iEACOP) intensifies the exploration
phase to escape from suboptimal search space areas where,
on the contrary, bEACOP gets stuck. Our results show that
iEACOP outperforms bEACOP on 27 out of 29 CEC 2017 test
suite benchmark functions, exhibiting comparable performance
against the three best algorithms of the CEC 2017 competition
on single-objective bound-constrained real-parameter numerical
optimization.