886 research outputs found
Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunities
Evolutionary algorithms (EA), a class of stochastic search methods based on
the principles of natural evolution, have received widespread acclaim for their
exceptional performance in various real-world optimization problems. While
researchers worldwide have proposed a wide variety of EAs, certain limitations
remain, such as slow convergence speed and poor generalization capabilities.
Consequently, numerous scholars actively explore improvements to algorithmic
structures, operators, search patterns, etc., to enhance their optimization
performance. Reinforcement learning (RL) integrated as a component in the EA
framework has demonstrated superior performance in recent years. This paper
presents a comprehensive survey on integrating reinforcement learning into the
evolutionary algorithm, referred to as reinforcement learning-assisted
evolutionary algorithm (RL-EA). We begin with the conceptual outlines of
reinforcement learning and the evolutionary algorithm. We then provide a
taxonomy of RL-EA. Subsequently, we discuss the RL-EA integration method, the
RL-assisted strategy adopted by RL-EA, and its applications according to the
existing literature. The RL-assisted procedure is divided according to the
implemented functions including solution generation, learnable objective
function, algorithm/operator/sub-population selection, parameter adaptation,
and other strategies. Finally, we analyze potential directions for future
research. This survey serves as a rich resource for researchers interested in
RL-EA as it overviews the current state-of-the-art and highlights the
associated challenges. By leveraging this survey, readers can swiftly gain
insights into RL-EA to develop efficient algorithms, thereby fostering further
advancements in this emerging field.Comment: 26 pages, 16 figure
A Keyword, Taxonomy and Cartographic Research Review of Sustainability Concepts for Production Scheduling in Manufacturing Systems
The concept of sustainability is defined as composed of three pillars: social, environmental, and economic. Social sustainability implies a commitment to equity in terms of several âinterrelated and mutually supportiveâ principles of a âsustainable societyâ; this concept includes attitude change, the Earthâs vitality and diversity conservation, and a global alliance to achieve sustainability. The social and environmental aspects of sustainability are related in the way sustainability indicators are related to âquality of lifeâ and âecological sustainabilityâ. The increasing interest in green and sustainable products and production has influenced research interests regarding sustainable scheduling problems in manufacturing systems. This study is aimed both at reducing pollutant emissions and increasing production efficiency: this topic is known as Green Scheduling. Existing literature research reviews on Green Scheduling Problems have pointed out both theoretical and practical aspects of this topic. The proposed work is a critical review of the scientific literature with a three-pronged approach based on keywords, taxonomy analysis, and research mapping. Specific research questions have been proposed to highlight the benefits and related objectives of this review: to discover the most widely used methodologies for solving SPGs in manufacturing and identify interesting development models, as well as the least studied domains and algorithms. The literature was analysed in order to define a map of the main research fields on SPG, highlight mainstream SPG research, propose an efficient view of emerging research areas, propose a taxonomy of SPG by collecting multiple keywords into semantic clusters, and analyse the literature according to a semantic knowledge approach. At the same time, GSP researchers are provided with an efficient view of emerging research areas, allowing them to avoid missing key research areas and focus on emerging ones
AnalĂœza dynamiky evoluÄnĂch algoritmĆŻ pomocĂ komplexnĂch sĂtĂ aplikovanĂœch na kombinatorickĂ© optimalizaÄnĂ problĂ©my
Import 05/08/2014This thesis explores the connection between Evolutionary Algorithms (EA's) and Complex Networks (CN's). EA's are bio-inspired algorithms which mimic naturally occurring phenomena in order to model and solve complex engineering tasks. One of its features is its population based paradigm. The behaviour of the population over the iterations is analysed in this thesis using CN analysis tools. Four distinct broad attributes are analysed; adjacency matrix, centralities, cliques and communities. Using these attributes, a number of experimentations and analysis were conducted, from which interesting information regarding population development, stagnation, network interconnection and hierarchical development was obtained. These data supported the concept of population dynamics and furthermore could be used for population and evolution control.Tato prĂĄce se zabĂœvĂĄ spojenĂm mezi evoluÄnĂmi algoritmy (EA) a komplexnĂmĂ sĂtÄmi (KS). EA jsou biologicky inspirovanĂ© algoritmy napodobujĂcĂ pĆirozenĂ© pĆĂrodnĂ jevy s cĂlem modelovat a ĆeĆĄit sloĆŸitĂ© technickĂ© problĂ©my. Jednou z jejich funkcĂ je snaha napodobit evoluÄnĂ dogma. ChovĂĄnĂ celĂ© populace je skrze jejĂ vĂœvoj sledovĂĄno pomocĂ nĂĄstrojĆŻ pro analĂœzu komplexnĂch sĂtĂ. AnalyzovĂĄny jsou tyto ÄtyĆi atributy: matice sjednocenĂ, centralita, kliky a komunity. Byla provedena Ćada experimentĆŻ a analĂœz, ze kterĂœch pomocĂ tÄchto atributĆŻ, byly zĂskĂĄny zajĂmavĂ© informace tĂœkajĂcĂ se vĂœvoje populace, stagnace, propojenĂ a hierarchie. ZĂskanĂĄ data nastĂnila koncepci populaÄnĂ dynamiky a dala by se vyuĆŸĂt ke kontrole samotnĂ© evoluce.440 - Katedra telekomunikaÄnĂ technikyvĂœborn
Evolutionary Computation 2020
Intelligent optimization is based on the mechanism of computational intelligence to refine a suitable feature model, design an effective optimization algorithm, and then to obtain an optimal or satisfactory solution to a complex problem. Intelligent algorithms are key tools to ensure global optimization quality, fast optimization efficiency and robust optimization performance. Intelligent optimization algorithms have been studied by many researchers, leading to improvements in the performance of algorithms such as the evolutionary algorithm, whale optimization algorithm, differential evolution algorithm, and particle swarm optimization. Studies in this arena have also resulted in breakthroughs in solving complex problems including the green shop scheduling problem, the severe nonlinear problem in one-dimensional geodesic electromagnetic inversion, error and bug finding problem in software, the 0-1 backpack problem, traveler problem, and logistics distribution center siting problem. The editors are confident that this book can open a new avenue for further improvement and discoveries in the area of intelligent algorithms. The book is a valuable resource for researchers interested in understanding the principles and design of intelligent algorithms
Application of lean scheduling and production control in non-repetitive manufacturing systems using intelligent agent decision support
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Lean Manufacturing (LM) is widely accepted as a world-class manufacturing paradigm, its currency and superiority are manifested in numerous recent success stories. Most lean tools including Just-in-Time (JIT) were designed for repetitive serial production systems. This resulted in a substantial stream of research which dismissed a priori the suitability of LM for non-repetitive non-serial job-shops. The extension of LM into non-repetitive production systems is opposed on the basis of the sheer complexity of applying JIT pull production control in non-repetitive systems fabricating a high variety of products. However, the application of LM in job-shops is not unexplored. Studies proposing the extension of leanness into non-repetitive production systems have promoted the modification of pull control mechanisms or reconfiguration of job-shops into cellular manufacturing systems. This thesis sought to address the shortcomings of the aforementioned approaches. The contribution of this thesis to knowledge in the field of production and operations management is threefold:
Firstly, a Multi-Agent System (MAS) is designed to directly apply pull production control to a good approximation of a real-life job-shop. The scale and complexity of the developed MAS prove that the application of pull production control in non-repetitive manufacturing systems is challenging, perplex and laborious. Secondly, the thesis examines three pull production control mechanisms namely, Kanban, Base Stock and Constant Work-in-Process (CONWIP) which it enhances so as to prevent system deadlocks, an issue largely unaddressed in the relevant literature. Having successfully tested the transferability of pull production control to non-repetitive manufacturing, the third contribution of this thesis is that it uses experimental and empirical data to examine the impact of pull production control on job-shop performance. The thesis identifies issues resulting from the application of pull control in job-shops which have implications for industry practice and concludes by outlining further research that can be undertaken in this direction
Multi-objective pareto ant colony system based algorithm for generator maintenance scheduling
Existing multi-objective Generator Maintenance Scheduling (GMS) models have considered unit commitment problem together with unit maintenance problem based on a periodic maintenance strategy. These models are inefficient because unit commitment does not undergo maintenance and periodic strategy cannot be applied on different types of generators. Present graph models cannot generate schedule for the multi-objective GMS models while existing Pareto Ant Colony System (PACS) algorithms were not able to consider the two problems separately. A multi-objective PACS algorithm based on sequential strategy which considers unit commitment and GMS problem separately is proposed to obtain solution for a proposed GMS model. A graph model is developed to generate the unitsâ maintenance schedule. The Taguchi and Grey Relational Analysis methods are proposed to tune the PACSâs parameters. The IEEE RTS 26, 32 and 36-unit dataset systems were used in the performance evaluation of the PACS algorithm. The performance of PACS algorithm was compared against four benchmark multi-objective algorithms including the Nondominated Sorting Genetic, Strength Pareto Evolutionary, Simulated Annealing, and Particle Swarm Optimization using the metrics grey relational grade (GRG), coverage, distance to Pareto front, Pareto spread, and number of non-dominated solutions. Friedman test was performed to determine the significance of the results. The multiobjective GMS model is superior than the benchmark model in producing the GMS schedule in terms of reliability, and violation objective functions with an average improvement between 2.68% and 92.44%. Friedman test using GRG metric shows significant better performance (p-values<0.05) for PACS algorithm compared to benchmark algorithms. The proposed models and algorithm can be used to solve the multi-objective GMS problem while the new parametersâ values can be used to obtain optimal or near optimal maintenance scheduling of generators. The proposed models and algorithm can be applied on different types of generating units to minimize the interruptions of energy and extend their lifespan
Advances in Condition Monitoring, Optimization and Control for Complex Industrial Processes
The book documents 25 papers collected from the Special Issue âAdvances in Condition Monitoring, Optimization and Control for Complex Industrial Processesâ, highlighting recent research trends in complex industrial processes. The book aims to stimulate the research field and be of benefit to readers from both academic institutes and industrial sectors
From metaheuristics to learnheuristics: Applications to logistics, finance, and computing
Un gran nombre de processos de presa de decisions en sectors estratĂšgics com el transport i la producciĂł representen problemes NP-difĂcils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurĂstiques sĂłn mĂštodes populars per a resoldre problemes d'optimitzaciĂł difĂcils en temps de cĂ lcul raonables. No obstant aixĂČ, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions sĂłn deterministes i conegudes. Aquests constitueixen supĂČsits forts que obliguen a treballar amb problemes simplificats. Com a conseqĂŒĂšncia, les solucions poden conduir a resultats pobres. Les simheurĂstiques integren la simulaciĂł a les metaheurĂstiques per resoldre problemes estocĂ stics d'una manera natural. AnĂ logament, les learnheurĂstiques combinen l'estadĂstica amb les metaheurĂstiques per fer front a problemes en entorns dinĂ mics, en quĂš els inputs poden dependre de l'estructura de la soluciĂł. En aquest context, les principals contribucions d'aquesta tesi sĂłn: el disseny de les learnheurĂstiques, una classificaciĂł dels treballs que combinen l'estadĂstica / l'aprenentatge automĂ tic i les metaheurĂstiques, i diverses aplicacions en transport, producciĂł, finances i computaciĂł.Un gran nĂșmero de procesos de toma de decisiones en sectores estratĂ©gicos como el transporte y la producciĂłn representan problemas NP-difĂciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurĂsticas son mĂ©todos populares para resolver problemas difĂciles de optimizaciĂłn de manera rĂĄpida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurĂsticas integran simulaciĂłn en metaheurĂsticas para resolver problemas estocĂĄsticos de una manera natural. De manera similar, las learnheurĂsticas combinan aprendizaje estadĂstico y metaheurĂsticas para abordar problemas en entornos dinĂĄmicos, donde los inputs pueden depender de la estructura de la soluciĂłn. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurĂsticas, una clasificaciĂłn de trabajos que combinan estadĂstica / aprendizaje automĂĄtico y metaheurĂsticas, y varias aplicaciones en transporte, producciĂłn, finanzas y computaciĂłn.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing
3rd Many-core Applications Research Community (MARC) Symposium. (KIT Scientific Reports ; 7598)
This manuscript includes recent scientific work regarding the Intel Single Chip Cloud computer and describes approaches for novel approaches for programming and run-time organization
- âŠ