A self-parametrization framework for meta-heuristics

Even while the scientific community has shown great interest in the analysis of meta-heuristics, the analysis of their parameterization has received little attention. It is the parameterization that will adapt a meta-heuristic to a problem, but it is still performed, mostly, empirically. There are multiple parameterization techniques; however, they are time-consuming, requiring considerable computational effort and they do not take advantage of the meta-heuristics that they parameterize. In order to approach the parameterization of meta-heuristics, in this paper, a self-parameterization framework is proposed. It will automatize the parameterization as an optimization problem, precluding the user from spending too much time on parameterization. The model will automate the parameterization through two meta-heuristics: A meta-heuristic of the solution space and one of the parameter space. To analyze the performance of the framework, a self-parameterization prototype was implemented. The prototype was compared and analyzed in a SP (scheduling problem) and in the TSP (traveling salesman problem). In the SP, the prototype found better solutions than those of the manually parameterized meta-heuristics, although the differences were not statistically significant. In the TSP, the self-parameterization prototype was more effective than the manually parameterized meta-heuristics, this time with statistically significant differences.This work was supported by national funds through the FCT - Fundação para a Ciência e Tecnologia through the R&D Units Project Scopes: UIDB/00319/2020, and EXPL/EME-SIS/1224/2021

Metaheuristics for online drive train efficiency optimization in electric vehicles

Utilization of electric vehicles provides a solution to several challenges in today’s individual mobility. However, ensuring maximum efficient operation of electric vehicles is required in order to overcome their greatest weakness: the limited range. Even though the overall efficiency is already high, incorporating DC/DC converter into the electric drivetrain improves the efficiency level further. This inclusion enables the dynamic optimization of the intermediate voltage level subject to the current driving demand (operating point) of the drivetrain. Moreover, the overall drivetrain efficiency depends on the setup of other drivetrain components’ electric parameters. Solving this complex problem for different drivetrain parameter setups subject to the current driving demand needs considerable computing time for conventional solvers and cannot be delivered in real-time. Therefore, basic metaheuristics are identified and applied in order to assure the optimization process during driving. In order to compare the performance of metaheuristics for this task, we adjust and compare the performance of different basic metaheuristics (i.e. Monte-Carlo, Evolutionary Algorithms, Simulated Annealing and Particle Swarm Optimization). The results are statistically analyzed and based on a developed simulation model of an electric drivetrain. By applying the bestperforming metaheuristic, the efficiency of the drivetrain could be improved by up to 30% compared to an electric vehicle without the DC/DC- converter. The difference between computing times vary between 30 minutes (for the Exhaustive Search Algorithm) to about 0.2 seconds (Particle Swarm) per operating point. It is shown, that the Particle Swarm Optimization as well as the Evolutionary Algorithm procedures are the best-performing methods on this optimization problem. All in all, the results support the idea that online efficiency optimization in electric vehicles is possible with regard to computing time and success probability

Two-Swim Operators in the Modified Bacterial Foraging Algorithm for the Optimal Synthesis of Four-Bar Mechanisms

This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem

An Adaptive GA in Partitioned Search Space

Evolutionary algorithms are population based meta-heuristics inspired from natural survival of fittest phenomena.Despite their reasonable performance, these algorithms suffer from some weaknesses including the need for finding the values of their parameters that affect their performance.A new algorithm is proposed that divide the search space into equal sized partitions.Each partition is assigned with two parameters that determine the intensification and diversification rates.The partitions will be intensified or diversified adaptively with regards to the corresponding parameters.Traditional crossover and mutation operators are replaced with two new parameter-free operators.The experiments conducted on a wide range of multi-modal and epistatic problems showed the superiority of the proposed method in comparison to other algorithms in literature

A prescription of methodological guidelines for comparing bio-inspired optimization algorithms

Bio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a new optimization technique with innovative elements is no longer enough. Apart from the novelty, results reported by the authors should be proven to achieve a significant advance over previous outcomes from the state of the art. Unfortunately, not all new proposals deal with this requirement properly. Some of them fail to select appropriate benchmarks or reference algorithms to compare with. In other cases, the validation process carried out is not defined in a principled way (or is even not done at all). Consequently, the significance of the results presented in such studies cannot be guaranteed. In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account. We expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field.This work was supported by grants from the Spanish Ministry of Science (TIN2016-8113-R, TIN2017-89517-P and TIN2017-83132-C2- 2-R) and Universidad Politécnica de Madrid (PINV-18-XEOGHQ-19- 4QTEBP). Eneko Osaba and Javier Del Ser-would also like to thank the Basque Government for its funding support through the ELKARTEK and EMAITEK programs. Javier Del Ser-receives funding support from the Consolidated Research Group MATHMODE (IT1294-19) granted by the Department of Education of the Basque Government

Differential Evolution: A Survey and Analysis

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based metaheuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. In particular, we present a state-of-the-art survey of the literature on DE and its recent advances, such as the development of adaptive, self-adaptive and hybrid techniques.http://dx.doi.org/10.3390/app810194

Large-Scale Evolutionary Optimization Using Multi-Layer Strategy Differential Evolution

Differential evolution (DE) has been extensively used in optimization studies since its development in 1995 because of its reputation as an effective global optimizer. DE is a population-based meta-heuristic technique that develops numerical vectors to solve optimization problems. DE strategies have a significant impact on DE performance and play a vital role in achieving stochastic global optimization. However, DE is highly dependent on the control parameters involved. In practice, the fine-tuning of these parameters is not always easy. Here, we discuss the improvements and developments that have been made to DE algorithms. The Multi-Layer Strategies Differential Evolution (MLSDE) algorithm, which finds optimal solutions for large scale problems. To solve large scale problems were grouped different strategies together and applied them to date set. Furthermore, these strategies were applied to selected vectors to strengthen the exploration ability of the algorithm. Extensive computational analysis was also carried out to evaluate the performance of the proposed algorithm on a set of well-known CEC 2015 benchmark functions. This benchmark was utilized for the assessment and performance evaluation of the proposed algorithm

Proposta de um algorítmo híbrido baseado em evolução diferencial para os problemas de p-medianas e de máxima cobertura

Orientadora : Profa. dra. Luzia Vidal de SouzaOrientador : Prof. Dr. Luiz Fernando NunesTese (doutorado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Métodos Numéricos em Engenharia. Defesa: Curitiba, 29/08/2014Inclui referênciasÁrea de concentração: Programação MatemáticaResumo: O estudo dos problemas de localização de instalações se relaciona diretamente com problemas organizacionais da sociedade, como por exemplo, a localização de escolas, postos de saúde, etc. Na sua forma geral, os problemas de P-Medianas e Máxima Cobertura são NP-hard e nas suas resoluções são utilizados métodos heurísticos. Os algoritmos de Evolução Diferencial (ED) são poderosos algoritmos de otimização evolucionária, propostos inicialmente, para problemas em espaços contínuos. Recentemente, têm sido propostas adaptações ao seu mecanismo de mutação diferencial para aplicação em problemas combinatórios. Este trabalho apresenta um novo algoritmo híbrido, utilizando algoritmos Evolução Diferencial e Busca Tabu, para a abordagem de problemas de P-Medianas e Máxima Cobertura. Introduz-se no operador de mutação diferencial de um algoritmo de Evolução Diferencial, o algoritmo Busca Tabu, com adaptações, a fim de que o mesmo possa ser aplicado para resolver problemas em um espaço de busca discreto. Testes computacionais foram realizados, com instâncias disponíveis na literatura, e comparados com outras meta-heurísticas e soluções ótimas obtidas com um modelo matemático. Os resultados encontrados sugerem que a técnica proposta é promissora e apropriada para a resolução dos problemas abordados, pois obteve-se na maioria dos testes soluções iguais ou melhores que alguns métodos presentes na literatura em tempos computacionais aceitáveis. Palavras Chave: Otimização Combinatória, Algoritmos Heurísticos, Localização de Instalações.The study of facility location problems is directly related to organizational problems of society, such as the location of schools, health centers , etc. . In its general form, the problem of P-Medians and Maximum Coverage is NP-hard, and heuristic methods are used to solve them. The Differential Evolution (DE) algorithms are powerful evolutionary optimization algorithms, originally proposed for problems in continuous spaces. Recently, it has been proposed adjustments that can be made to the mechanism of differential mutation for its application to combinational problems. This paper presents a new hybrid algorithm, using Differential Evolution Algorithms and Tabu Search, to address problems of P-Medians and Maximum Coverage. Be introduced to the operator of a differential mutation algorithm Differential Evolution, the Tabu Search algorithm with adaptations, so that it can be applied to solve problems in a discrete search space. Computational tests were performed, with instances available in the literature, and compared with other meta-heuristics and optimal solutions obtained from a mathematical model. The results suggest that the proposed technique is promising and appropriate for the resolution of the problems addressed, as was obtained in most testing solutions equal or better than some methods from the literature in acceptable computational time. Keywords : Combinatorial Optimization, Heuristic Algorithms, Location of Facilities

Online evolution of robot behaviour

Tese de mestrado em Engenharia Informática (Interação e Conhecimento), apresentada à Universidade de Lisboa, através da Faculdade de Ciências, 2012In this dissertation, we propose and evaluate two novel approaches to the online synthesis of neural controllers for autonomous robots. The first approach is odNEAT, an online, distributed, and decentralized version of NeuroEvolution of Augmenting Topologies (NEAT). odNEAT is an algorithm for online evolution in groups of embodied agents such as robots. In odNEAT, agents have to solve the same task, either individually or collectively. While previous approaches to online evolution of neural controllers have been limited to the optimization of weights, odNEAT evolves both weights and network topology. We demonstrate odNEAT through a series of simulation-based experiments in which a group of e-puck-like robots must perform an aggregation task. Our results show that robots are capable of evolving effective aggregation strategies and that sustainable behaviours evolve quickly. We show that odNEAT approximates the performance of rtNEAT, a similar but centralized method. We also analyze the contribution of each algorithmic component on the performance through a series of ablation studies. In the second approach, we extend our previous method and combine online evolution of weights and network topology (odNEAT) with neuromodulated learning. We demonstrate our method through a series of experiments in which a group of simulated robots must perform a dynamic concurrent foraging task. In this task, scattered food items periodically change their nutritive value or become poisonous. Our results show that when neuromodulated learning is employed, neural controllers are synthesized faster than by odNEAT alone. We demonstrate that the online evolutionary process is capable of generating controllers that adapt to the periodic task changes. We evaluate the performance both in a single robot setup and in a multirobot setup. An analysis of the evolved networks shows that they are characterized by specialized modulatory neurons that exclusively regulate online learning in the output neurons

Predição da estrutura de proteínas off-lattice usando evolução diferencial multiobjetivo adaptativa

Protein Structure Prediction (PSP) can be considered one of the most challenging problems in Bioinformatics nowadays. When a protein is in its conformation state, the free energy is minimized. Evaluation of protein conformation is generally performed based on two values of the estimated free energy, i.e., those provided by intra and intermolecular interactions among atoms. Some recent experimental studies show that these interactions are in conflit, justifying the use of multiobjective optimization approaches to solve PSP. In this case, the energy optimization is performed separately, different from the mono-objective optimization which considers the sum of free energy. Differential Evolution (DE) is a technique based on Evolutionary Computation and represents an interesting alternative to solve multiobjective PSP. In this work, an optimizer based on DE is proposed to solve the PSP problem. Due to the great number of parameters, typical for evolutionary algorithms, this work also investigates adaptive parameters strategies. In experiments, a simple approach based on ED is evaluated for PSP. An evolution for this method, which incorporates concepts of the MOEA/D algorithm and parameter adaptation techniques is tested for a set of benchmarks in the multiobjective optimization context. The preliminary results for PSP (for six real proteins) are promising and those obtained for the benchmark set stands the proposed approach as a candidate to the state-of-art for multiobjective optimization.Fundação AraucáriaA Predição da Estrutura das Proteínas, conhecida como PSP (Protein Structure Prediction) pode ser considerada um dos problemas mais desafiadores da Bioinformática atualmente. Quando uma proteína está em seu estado de conformação nativa, a energia livre tende para um valor mínimo. Em geral, a predição da conformação de uma proteína por métodos computacionais é feita pela estimativa de dois valores de energia livre que são provenientes das interações intra e intermoleculares entre os átomos. Alguns estudos recentes indicam que estas interações estão em conflito, justificando o uso de abordagens baseadas em otimização multiobjetivo para a solução do PSP. Neste caso, a otimização destas energias é realizada separadamente, diferente da formulação mono-objetivo que considera a soma das energias. A Evolução Diferencial (ED) é uma técnica baseada em Computação Evolucionária e representa uma alternativa interessante para abordar o PSP. Este trabalho busca desenvolver um otimizador baseado no algoritmo de ED para o problema da Predição da Estrutura de Proteínas multiobjetivo. Este trabalho investiga ainda estratégias baseadas em parâmetros adaptativos para a evolução diferencial. Nicialmente avalia-se uma abordagem simples baseada em ED proposta para a solução do PSP. Uma evolução deste método que incorpora conceitos do algoritmo MOEA/D e adaptação de parâmetros é testada em um conjunto de problemas benchmark de otimização multiobjetivo. Os resultados preliminares obtidos para o PSP (para seis proteínas reais) são promissores e aqueles obtidos para o conjunto benchmark colocam a abordagem proposta como candidata para otimização multiobjetivo