An overview of population-based algorithms for multi-objective optimisation

In this work we present an overview of the most prominent population-based algorithms and the methodologies used to extend them to multiple objective problems. Although not exact in the mathematical sense, it has long been recognised that population-based multi-objective optimisation techniques for real-world applications are immensely valuable and versatile. These techniques are usually employed when exact optimisation methods are not easily applicable or simply when, due to sheer complexity, such techniques could potentially be very costly. Another advantage is that since a population of decision vectors is considered in each generation these algorithms are implicitly parallelisable and can generate an approximation of the entire Pareto front at each iteration. A critique of their capabilities is also provided

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

A research survey: review of flexible job shop scheduling techniques

In the last 25 years, extensive research has been carried out addressing the flexible job shop scheduling (JSS) problem. A variety of techniques ranging from exact methods to hybrid techniques have been used in this research. The paper aims at presenting the development of flexible JSS and a consolidated survey of various techniques that have been employed since 1990 for problem resolution. The paper comprises evaluation of publications and research methods used in various research papers. Finally, conclusions are drawn based on performed survey results. A total of 404 distinct publications were found addressing the FJSSP. Some of the research papers presented more than one technique/algorithm to solve the problem that is categorized into 410 different applications. Selected time period of these research papers is between 1990 and February 2014. Articles were searched mainly on major databases such as SpringerLink, Science Direct, IEEE Xplore, Scopus, EBSCO, etc. and other web sources. All databases were searched for “flexible job shop” and “scheduling” in the title an

When Evolutionary Computing Meets Astro- and Geoinformatics

International audienceKnowledge discovery from data typically includes solving some type of an optimization problem that can be efficiently addressed using algorithms belonging to the class of evolutionary and bio-inspired computation. In this chapter, we give an overview of the various kinds of evolutionary algorithms, such as genetic algorithms, evolutionary strategy, evolutionary and genetic programming, differential evolution, and coevolutionary algorithms, as well as several other bio-inspired approaches, like swarm intelligence and artificial immune systems. After elaborating on the methodology, we provide numerous examples of applications in astronomy and geoscience and show how these algorithms can be applied within a distributed environment, by making use of parallel computing, which is essential when dealing with Big Data

Holistic, data-driven, service and supply chain optimisation: linked optimisation.

The intensity of competition and technological advancements in the business environment has made companies collaborate and cooperate together as a means of survival. This creates a chain of companies and business components with unified business objectives. However, managing the decision-making process (like scheduling, ordering, delivering and allocating) at the various business components and maintaining a holistic objective is a huge business challenge, as these operations are complex and dynamic. This is because the overall chain of business processes is widely distributed across all the supply chain participants; therefore, no individual collaborator has a complete overview of the processes. Increasingly, such decisions are automated and are strongly supported by optimisation algorithms - manufacturing optimisation, B2B ordering, financial trading, transportation scheduling and allocation. However, most of these algorithms do not incorporate the complexity associated with interacting decision-making systems like supply chains. It is well-known that decisions made at one point in supply chains can have significant consequences that ripple through linked production and transportation systems. Recently, global shocks to supply chains (COVID-19, climate change, blockage of the Suez Canal) have demonstrated the importance of these interdependencies, and the need to create supply chains that are more resilient and have significantly reduced impact on the environment. Such interacting decision-making systems need to be considered through an optimisation process. However, the interactions between such decision-making systems are not modelled. We therefore believe that modelling such interactions is an opportunity to provide computational extensions to current optimisation paradigms. This research study aims to develop a general framework for formulating and solving holistic, data-driven optimisation problems in service and supply chains. This research achieved this aim and contributes to scholarship by firstly considering the complexities of supply chain problems from a linked problem perspective. This leads to developing a formalism for characterising linked optimisation problems as a model for supply chains. Secondly, the research adopts a method for creating a linked optimisation problem benchmark by linking existing classical benchmark sets. This involves using a mix of classical optimisation problems, typically relating to supply chain decision problems, to describe different modes of linkages in linked optimisation problems. Thirdly, several techniques for linking supply chain fragmented data have been proposed in the literature to identify data relationships. Therefore, this thesis explores some of these techniques and combines them in specific ways to improve the data discovery process. Lastly, many state-of-the-art algorithms have been explored in the literature and these algorithms have been used to tackle problems relating to supply chain problems. This research therefore investigates the resilient state-of-the-art optimisation algorithms presented in the literature, and then designs suitable algorithmic approaches inspired by the existing algorithms and the nature of problem linkages to address different problem linkages in supply chains. Considering research findings and future perspectives, the study demonstrates the suitability of algorithms to different linked structures involving two sub-problems, which suggests further investigations on issues like the suitability of algorithms on more complex structures, benchmark methodologies, holistic goals and evaluation, processmining, game theory and dependency analysis

Otimização multiobjetivo aplicada ao planejamento sistemático de conservação para espécies de plantas do cerrado brasileiro

Tese (doutorado)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2015.Nesta tese, propôs-se a aplicação de conceitos de Otimização Multiobjetivo (MOO) e de Computação Bioinspirada a problemas de Planejamento Sistemático de Conservação (SCP). Foram estudados três problemas específicos. No primeiro, buscou-se o menor conjunto de populações locais a serem conservadas para representar a diversidade genética de uma espécie vegetal do Cerrado. O método proposto foi capaz de identificar uma maior diversidade de soluções com a quantidade mínima de populações ao mesmo tempo em que refinou os resultados, indicando as combinações com maior diversidade intraespecífica e maior possibilidade de persistência ao longo do tempo. No segundo problema, buscou-se: (i) selecionar um conjunto de amostras geneticamente complementares a uma coleção de germoplasma de plantas já existente; (ii) definir uma core collection para uma coleção de germoplasma. Com a utilização de MOO foi possível identificar os indivíduos exatos que deveriam ser selecionados para complementar o germoplasma. Ademais, definiu-se um protocolo para tratar um grande volume de amostras a fim de estabelecer uma core collection. A abordagem proposta pode ser usada para construir core collections com máxima riqueza alélica, bem como ser estendido a casos de conservação in situ. Por fim, no terceiro problema, SCP foi associado à estimativa da ocorrência de espécies projetada para o futuro com base em simulações climáticas objetivando definir prioridades de conservação. O método proposto identificou locais com: (i) alta prioridade para conservação; (ii) risco significativo de investimento; e, (iii) que poderiam tornar-se atrativos no futuro. Foi proposto, também, um algoritmo multiobjetivo baseado em Sistemas Imunológicos Artificiais, o Multi-Objective Artificial Immune System (MAIS). MOO permitiu trabalhar com instâncias de problemas com mais de duas dimensões, possibilitando maior confiabilidade na indicação do portfolio de soluções, aumentando, assim, o poder de decisão do método computacional e a qualidade da informação fornecida aos tomadores de decisão. O presente trabalho é pioneiro no país ao resolver problemas de SCP usando técnicas avançadas de otimização, colaborando para a implantação da área de Ecoinformática no Brasil.This thesis proposes a more sophisticated, yet general, solution to the systematic conservation planning problem (SCP) based on multi-objective optimization (MOO) and bio-inspired computing. We worked with three problems using data from plants of the Brazilian Cerrado biome. In the first problem, we looked for the smallest set of local populations of a plant species aiming its conservation. The method was able to find a larger portfolio of solutions and to refine the results as well, indicating solutions with more intra-specific diversity and higher probability of persistence throughout time. In the second problem, we aimed: (i) to select a set of individuals genetically complementary to an existing plant germplasm collection; and, (ii) to define a core collection for a germplasm collection. We were able to identify within a population of several individuals, the exact accessions/samples that should be chosen in order to preserve the species diversity. Moreover, we defined a method (a protocol) to deal with large amounts of accessions in the context of MOO. The proposed approach can be used to help constructing collections with maximal allelic richness and can also be extended to the in situ conservation. Finally, in the third problem, we applied MOO to SCP associated to climate forecasting, in a dynamic spatial prioritization analysis for biodiversity conservation. Our method was able to identify sites: (i) of high priority for conservation; (ii) with significant risk of investment; and, (iii) that may become attractive in the future. We also proposed a constrained multi-objective artificial immune system algorithm (MAIS). The MOO approach to SCP increases reliability by including additional objectives, which while increasing the complexity, significantly augments the amount and quality of information used to provide users with an improved decision support system. This thesis is pioneer in solving the SCP problem using advanced optimization techniques contributing to the insertion and consolidation of the new area of ecoinformatics in Brazil

Efficient Learning Machines

Computer scienc

ADAPTIVE, MULTI-OBJECTIVE JOB SHOP SCHEDULING USING GENETIC ALGORITHMS

This research proposes a method to solve the adaptive, multi-objective job shop scheduling problem. Adaptive scheduling is necessary to deal with internal and external disruptions faced in real life manufacturing environments. Minimizing the mean tardiness for jobs to effectively meet customer due date requirements and minimizing mean flow time to reduce the lead time jobs spend in the system are optimized simultaneously. An asexual reproduction genetic algorithm with multiple mutation strategies is developed to solve the multi-objective optimization problem. The model is tested for single day and multi-day adaptive scheduling. Results are compared with those available in the literature for standard problems and using priority dispatching rules. The findings indicate that the genetic algorithm model can find good solutions within short computational time

Decarbonising Future Power Systems by Demand Side Management in Smart Grid

Carbon emission reduction is an urgent global task. Renewable energy sources integration can promote the transformation of cleaner and greener power system. But the time-varying nature of these sources causes indeterminacy problems. Smart grid is a powerful tool that can deal with these problems in electricity aspect. One of the key smart grid technologies is demand side management. How to use demand side management to regulate and decarbonise the power system is the main point of this thesis. In order to integrate renewable energy sources, a day-ahead electricity market scheme is proposed, involving the utility, the demand response aggregator and customers. This model leads to a multiobjective optimization problem, which is solved by an artificial immune algorithm. The simulation results confirm the feasibility and robustness of the proposed model. All participants can benefit from it, and the system power peak to average ratio can be reduced. In order to realize the carbon emission reduction, a system model for annual fuel sources scheduling and operational policy making of electricity generation is established, considering the economic, environmental and social aspects. A minimum Manhattan distance approach is proposed to select the final solution. The impacts of carbon tax and renewable obligation on carbon emission, generation cost and electricity bill are examined. These can reveal the proper strategy for deciding renewable energy source and carbon emission related policies. After that, a carbon emission flow model is introduced to facilitate the analysis and assessment of demand side management’s impacts on carbon emission reduction. The time sensitivity of carbon emission in both generation side and customer side are obtained. The daily case and seasonal case are presented. The simulation results show that the load curtailment and load shift approaches can effectively reduce the carbon emission

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp