Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method

Álvaro Gómez-Rubio,Ricardo Soto,David Mancilla,Broderick Crawford,Carlos Castro,Adrián Jaramillo,Rodrigo Olivares

doi:10.3390/math10020274

Abstract

In the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in adequate time, which do exist in different industries. Big data has demonstrated its efficiency in solving different concerns in information management. In this paper, an approach based on multiprocessing is proposed wherein clusterization and parallelism are used together to improve the search process of metaheuristics when solving large instances of complex optimization problems, incorporating collaborative elements that enhance the quality of the solution. The proposal deals with machine learning algorithms to improve the segmentation of the search space. Particularly, two different clustering methods belonging to automatic learning techniques, are implemented on bio-inspired algorithms to smartly initialize their solution population, and then organize the resolution from the beginning of the search. The results show that this approach is competitive with other techniques in solving a large set of cases of a well-known NP-hard problem without incorporating too much additional complexity into the metaheuristic algorithms.

Highlights

In the optimization sphere, the solving of distinctly large and complex problems involves important information management issues [1]
The solving of distinctly large and complex problems involves important information management issues [1]. Most of these problems are currently tackled using metaheuristic algorithms, which attempt to optimize the values obtained by an objective function; this is done through an exploration of a large number of potential solutions, carried out by a population of agents [2]
This research aims to demonstrate that the subdivision of the solution space into homogeneous groups, combined with the cooperation between the nodes generated according to the results found with the proposed framework, versus those by the standard versions of metaheuristics, allows for the improvement of the exploration and exploitation characteristics independent of the algorithms used in the process

Summary

Introduction

The solving of distinctly large and complex problems involves important information management issues [1]. Most of these problems are currently tackled using metaheuristic algorithms, which attempt to optimize the values obtained by an objective function; this is done through an exploration of a large number of potential solutions, carried out by a population of agents [2] This exploration is governed by a movement/disturbance operator, which decides the regions to be analyzed. The objective of this study is to present a framework to improve the efficiency and effectiveness of the metaheuristic search process by increasing the number of global optima found and reducing the time or computational costs taken for the same This is done by implementing the search process in several runs of metaheuristic algorithms, in an autonomous and parallel way (nodes). Parallel execution and exploitation of these segregated spaces are performed by distributing the searches through parallelism based on big data tools

Objectives

Results

Conclusion