Selection methods and diversity preservation in many-objective evolutionary algorithms

Purpose – One of the main components of multi-objective, and therefore, many-objective evolutionary algorithms is the selection mechanism. It is responsible for performing two main tasks simultaneously. First, it has to promote convergence by selecting solutions which are as close as possible to the Pareto optimal set. And second, it has to promote diversity in the solution set provided. In the current work, an exhaustive study that involves the comparison of several selection mechanisms with different features is performed. Particularly, Pareto-based and indicator-based selection schemes, which belong to well-known multi-objective optimisers, are considered. Design/methodology/approach – Each of those mechanisms is incorporated into a common multi-objective evolutionary algorithm framework. The main goal of the study is to measure the diversity preserved by each of those selection methods when addressing many-objective optimisation problems. The Walking Fish Group (WFG) test suite, a set of optimisation problems with a scalable number of objective functions, is taken into account to perform the experimental evaluation. Findings – The computational results highlight that the the reference-point-based selection scheme of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II), where the crowding distance is replaced by the Euclidean distance, are able to provide the best performance, not only in terms of diversity preservation, but also in terms of convergence. Originality/value – The performance provided by the use of the Euclidean distance as part of the selection scheme indicates this is a promising line of research and, to the best of our knowledge, it has not been investigated yet

Impact of selection methods on the diversity of many-objective Pareto set approximations

Selection methods are a key component of all multi-objective and, consequently, many-objective optimisation evolutionary algorithms. They must perform two main tasks simultaneously. First of all, they must select individuals that are as close as possible to the Pareto optimal front (convergence). Second, but not less important, they must help the evolutionary approach to provide a diverse population. In this paper, we carry out a comprehensive analysis of state-of-the-art selection methods with different features aimed to determine the impact that this component has on the diversity preserved by well-known multi-objective optimisers when dealing with many-objective problems. The algorithms considered herein, which incorporate Pareto-based and indicator-based selection schemes, are analysed through their application to the Walking Fish Group (WFG) test suite taking into account an increasing number of objective functions. Algorithmic approaches are assessed via a set of performance indicators specifically proposed for measuring the diversity of a solution set, such as the Diversity Measure and the Diversity Comparison Indicator. Hyper-volume, which measures convergence in addition to diversity, is also used for comparison purposes. The experimental evaluation points out that the reference-point-based selection scheme of the Non-dominated Sorting Genetic Algorithm III (NSGA-III) and a modified version of the Non-dominated Sorting Genetic Algorithm II (NSGA-II), where the the crowding distance is replaced by the Euclidean distance, yield the best results

Comparison between Single and Multi-Objective Evolutionary Algorithms to Solve the Knapsack Problem and the Travelling Salesman Problem

One of the main components of most modern Multi-Objective Evolutionary Algorithms (MOEAs) is to maintain a proper diversity within a population in order to avoid the premature convergence problem. Due to this implicit feature that most MOEAs share, their application for Single-Objective Optimization (SO) might be helpful, and provides a promising field of research. Some common approaches to this topic are based on adding extra—and generally artificial—objectives to the problem formulation. However, when applying MOEAs to implicit Multi-Objective Optimization Problems (MOPs), it is not common to analyze how effective said approaches are in relation to optimizing each objective separately. In this paper, we present a comparative study between MOEAs and Single-Objective Evolutionary Algorithms (SOEAs) when optimizing every objective in a MOP, considering here the bi-objective case. For the study, we focus on two well-known and widely studied optimization problems: the Knapsack Problem (KNP) and the Travelling Salesman Problem (TSP). The experimental study considers three MOEAs and two SOEAs. Each SOEA is applied independently for each optimization objective, such that the optimized values obtained for each objective can be compared to the multi-objective solutions achieved by the MOEAs. MOEAs, however, allow optimizing two objectives at once, since the resulting Pareto fronts can be used to analyze the endpoints, i.e., the point optimizing objective 1 and the point optimizing objective 2. The experimental results show that, although MOEAs have to deal with several objectives simultaneously, they can compete with SOEAs, especially when dealing with strongly correlated or large instances

A similarity-based neighbourhood search for enhancing the balance exploration–exploitation of differential evolution

The success of search-based optimisation algorithms depends on appropriately balancing exploration and exploitation mechanisms during the course of the search. We introduce a mechanism that can be used with Differential Evolution (de) algorithms to adaptively manage the balance between the diversification and intensification phases, depending on current progress. The method—Similarity-based Neighbourhood Search (sns)—uses information derived from measuring Euclidean distances among solutions in the decision space to adaptively influence the choice of neighbours to be used in creating a new solution. sns is integrated into explorative and exploitative variants of jade, one of the most frequently used adaptive de approaches. Furthermore, shade, which is another state-of-the-art adaptive de variant, is also considered to assess the performance of the novel sns. A thorough experimental evaluation is conducted using a well-known set of large-scale continuous problems, revealing that incorporating sns allows the performance of both explorative and exploitative variants of de to be significantly improved for a wide range of the test-cases considered. The method is also shown to outperform variants of de that are hybridised with a recently proposed global search procedure, designed to speed up the convergence of that algorithm

A fuzzy logic controller applied to a diversity-based multi-objective evolutionary algorithm for single-objective optimisation

In recent years, Multi-Objective Evolutionary Algorithms (MOEAS) that consider diversity as an objective have been used to tackle single-objective optimisation prob- lems. The ability to deal with premature convergence has been greatly improved with these schemes. However, they usually increase the number of free parameters that need to be tuned. To improve results and avoid the tedious hand- tuning of algorithms, the use of automated parameter con- trol approaches that are able to adapt parameter values dur- ing the course of an evolutionary run are becoming more common in the field of Evolutionary Computation (EC). This research focuses on the application of parameter control approaches to diversity-based moeas. Two external parame- ter control methods are investigated; a novel method based on Fuzzy Logic and a recently proposed Hyper-heuristic. These are compared to an internal control method that uses self- adaptation. An extensive comparison of the three methods is carried out using a set of single-objective benchmark prob- lems of diverse complexity. Analyses include comparisons to a wide range of schemes with fixed parameters and to a single-objective approach. The results show that the fuzzy logic and hyper-heuristic methods are able to find similar or better solutions than the fixed parameter methods for a sig- nificant number of problems, with considerable savings in computational resources and time, whereas the self-adaptive strategy provides little benefit. Finally, we also demonstrate that the controlled diversity-based moea outperforms the single-objective scheme in most cases, thus showing the ben- efits of solving single-objective problems through diversity-based multi-objective schemes

El proyecto Piens@ Computacion@LLmente

Este trabajo describe el proyecto ‘Piens@ Computacion@LLmente’, con el que se introduce en el Pensamiento Computacional a niños de 4º de primaria y de 2º de la ESO. Para ello, se plantean diferentes ejercicios en los que hay que desarrollar una solución diseñando un algoritmo y codificándolo mediante un lenguaje de programación visual. Se proponen tanto desafíos robóticos, cómo actividades en las que no se requiere una computadora. Además, se ejecutan las intervenciones en dos modalidades, una guiada (enseñanza tradicional) y otra por descubrimiento. Finalmente, se analiza el interés que estas actividades han despertado en los alumnos, diferenciando edades, género y modalidades

Computational Thinking and User Interfaces: A Systematic Review

Contribution: This document presents a systematic bibliographic review that demonstrates the need to conduct research on how the user experience impacts the development of computational thinking. Background: In the field of computer science, computational thinking is defined as a method that enhances problem-solving skills, system design, and human behavior understanding. Over the last few decades, several tools have been proposed for the development of computational thinking skills; however, there is no area of study that evaluates the implications or the impact that these types of platforms have on users belonging to any knowledge area. Research Question: Do user interfaces influence the development of computational thinking skills? Methodology: To address this issue, a systematic review of the literature was conducted using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology for analyzing and evaluating scientific publications. Findings: The results show that despite the dearth of literature on the subject, the specific design of a user interface has a significant impact on the development of computational thinking. Bearing the above in mind, it is necessary to conduct research that delves more deeply into the effects caused by the technologies that are used to develop computational thinking, this being a line of research that is worthy of consideration

Optimising Real-World Traffic Cycle Programs by Using Evolutionary Computation

Traffic congestion, and the consequent loss of time, money, quality of life, and higher pollution, is currently one of the most important problems in cities, and several approaches have been proposed to reduce it. In this paper, we propose a novel formulation of the traffic light scheduling problem in order to alleviate it. This novel formulation of the problem allows more realistic scenarios to be modeled, and as a result, it becomes much harder to solve in comparison to previous formulations. The proposal of more advanced and efficient techniques than those applied in past research is thus required. We propose the application of diversity-based multi-objective optimizers, which have shown to provide promising results when addressing single-objective problems. The wide experimental evaluation performed over a set of real-world instances demonstrates the good performance of our proposed diversity-based multi-objective method to tackle traffic at a large scale, especially in comparison to the best-performing single-objective optimizer previously proposed in the literature. Consequently, in this paper, we provide new state-of-the-art algorithmic schemes to address the traffic light scheduling problem that can deal with a whole city, instead of just a few streets and junctions, with a higher level of detail than the one found in present studies due to our micro-analysis of streets

On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems

Migrating Birds Optimisation (mbo) is a nature-inspired approach which has been shown to be very effective when solving a variety of combinatorial optimisation problems. More recently, an adaptation of the algorithm has been proposed that enables it to deal with continuous search spaces. We extend this work in two ways.Firstly, a novel leader replacement strategy is proposed to counter the slow convergence of the existing mbo algorithms due to low selection pressure. Secondly, mbo is hybridised with adaptive neighbourhood operators borrowed from Differential Evolution (de) that promote exploration and exploitation. The new variants are tested on two sets of continuous large scale optimisation problems. Results show that mbo variants using adaptive, exploration-based operators outperform de on the cec benchmark suite with 1000variables. Further experiments on a second suite of 19 problems show that mbo variants outperform de on 90% of these test-cases