A GRASP-based memetic algorithm with path relinking for the far from most string problem

Gallardo, José E.; Cotta, Carlos

doi:10.1016/j.engappai.2015.01.020

Cited by 15 publications

(1 citation statement)

References 41 publications

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“…In order to know if there are significant differences between the results achieved by the algorithms and rank them, an Aligned Friedman Rank Test was performed, following [46,58,59]. This is a multiple-comparison non-parametric test, whose null hypothesis considers that there are no differences in the rankings of the algorithms.…”

Section: Final Comparisonmentioning

confidence: 99%

One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

Mencía

2021

Mathematics

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This paper addresses the problem of scheduling a set of jobs on a machine with time-varying capacity, with the goal of minimizing the total tardiness objective function. This problem arose in the context scheduling the charging times of a fleet of electric vehicles and it is NP-hard. Recent work proposed an efficient memetic algorithm for solving the problem, combining a genetic algorithm and a local search method. The local search procedure is based on swapping consecutive jobs on a C-path, defined as a sequence of consecutive jobs in a schedule. Building on it, this paper develops new memetic algorithms that stem from new local search procedures also proposed in this paper. The local search methods integrate several mechanisms to make them more effective, including a new condition for swapping pairs of jobs, a hill climbing approach, a procedure that operates on several C-paths and a method that interchanges jobs between different C-paths. As a result, the new local search methods enable the memetic algorithms to reach higher-quality solutions. Experimental results show significant improvements over existing approaches.

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Section: Final Comparisonmentioning

confidence: 99%

One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

Mencía

2021

Mathematics

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Memetic Algorithms: A Contemporary Introduction

Cotta

Gallardo

Mathieson

et al. 2016

Wiley Encyclopedia of Electrical and Electronics Engineering

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Memetic algorithms (MAs) constitute a search and optimization paradigm based on the orchestrated interplay between global and local search components, and have the exploitation of specific problem knowledge as one of their central tenets. MAs can take different forms although a classical incarnation involves the integration of independent search processes within a population‐based optimization approach. We discuss this basic structure as well as several of the issues arising in the design process. This paves the way for providing a glimpse of some algorithmic extensions of this basic scheme. After providing an overview of the numerous practical applications of MAs, we close this article with some current perspectives of these techniques.

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Memetic Algorithms for Business Analytics and Data Science: A Brief Survey

Moscato

Mathieson

2019

Business and Consumer Analytics: New Ideas

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A GRASP-based memetic algorithm with path relinking for the far from most string problem

Cited by 15 publications

References 41 publications

One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

One-Machine Scheduling with Time-Dependent Capacity via Efficient Memetic Algorithms

Memetic Algorithms: A Contemporary Introduction

Memetic Algorithms for Business Analytics and Data Science: A Brief Survey

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