Improved Method for Parallelization of Evolutionary Metaheuristics

Abstract

This paper introduces a method for the distribution of any and all population-based metaheuristics. It improves on the naive approach, independent multiple runs, while adding negligible overhead. Existing methods that coordinate instances across a cluster typically require some compromise of more complex design, higher communication loads, and solution propagation rate, requiring more work to develop and more resources to run. The aim of the new method is not to achieve state-of-the-art results, but rather to provide a better baseline method than multiple independent runs. The main concept of the method is that one of the instances receives updates with the current best solution of all other instances. This work describes the general approach and its particularization to both genetic algorithms and ant colony optimization for solving Traveling Salesman Problems (TSPs). It also includes extensive tests on the TSPLIB benchmark problems of resulting quality of the solutions and anytime performance (solution quality versus time to reach it). These tests show that the new method yields better solutions for about two thirds of the problems and equivalent solutions in the remaining third, and consistently exhibits better anytime performance.

generic heart diseases prediction and application of genetic algorithms in healthcare systems

“…There are several selection techniques; consequently, the objective of all is to map-out the fitness values to individuals based on a fitness function and the selected fittest offspring's genetic alterations in chromosomes or features will occur through crossover and mutations to produce another generation. An optimal solution would have to be obtained due to it iterative process will continue until the fittest individual is formulated or the maximum number of generations is achieved [17][18]. Additionally, GA is very important to predict certain parameters for a given generation and determine which variable will attain the best performance especially in disease predictions with combinatory algorithm of GA and ML in healthcare domain.…”

Automatic Group Organization for Collaborative Learning Applying Genetic Algorithm Techniques and the Big Five Model

“…Genetic algorithms were described by Holland [30] and are considered as a computational model family inspired by evolutionary biology [31]. To date, their use has spread beyond the original conception, as a more general type of evolutionary algorithm that attempts to simulate Darwinian evolution and natural selection through the recombination and mutation of individuals [32]. These algorithms use a data structure to encode the potential solutions to the problem in question, generally a vector, as a chromosome and apply recombination operators seeking to preserve the critical information that guides towards a satisfactory solution [33].…”

A Chaotic Krill Herd Optimization Algorithm for Global Numerical Estimation of the Attraction Domain for Nonlinear Systems

“…Mostly, advanced intelligent techniques are developed to deal with the design of these kinds of optimization processes. In view of their methodology, optimization techniques can be classified into several main categories: (1) analytical or deterministic, (2) heuristic or random advanced methods and (3) Multi-Objective or single objective optimization [1]. The first class, based mainly on Mathematics 2021, 9, 1743 2 of 30 the gradient theory, are known to be "strict step" methods.…”