Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm

On the role of metaheuristic optimization in bioinformatics

Metaheuristic algorithms are employed to solve complex and large-scale optimization problems in many different fields, from transportation and smart cities to finance. This paper discusses how metaheuristic algorithms are being applied to solve different optimization problems in the area of bioinformatics. While the text provides references to many optimization problems in the area, it focuses on those that have attracted more interest from the optimization community. Among the problems analyzed, the paper discusses in more detail the molecular docking problem, the protein structure prediction, phylogenetic inference, and different string problems. In addition, references to other relevant optimization problems are also given, including those related to medical imaging or gene selection for classification. From the previous analysis, the paper generates insights on research opportunities for the Operations Research and Computer Science communities in the field of bioinformatics

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Bio-inspired computation: where we stand and what's next

In recent years, the research community has witnessed an explosion of literature dealing with the adaptation of behavioral patterns and social phenomena observed in nature towards efficiently solving complex computational tasks. This trend has been especially dramatic in what relates to optimization problems, mainly due to the unprecedented complexity of problem instances, arising from a diverse spectrum of domains such as transportation, logistics, energy, climate, social networks, health and industry 4.0, among many others. Notwithstanding this upsurge of activity, research in this vibrant topic should be steered towards certain areas that, despite their eventual value and impact on the field of bio-inspired computation, still remain insufficiently explored to date. The main purpose of this paper is to outline the state of the art and to identify open challenges concerning the most relevant areas within bio-inspired optimization. An analysis and discussion are also carried out over the general trajectory followed in recent years by the community working in this field, thereby highlighting the need for reaching a consensus and joining forces towards achieving valuable insights into the understanding of this family of optimization techniques

Parallel Multi-Objective Evolutionary Algorithms: A Comprehensive Survey

Multi-Objective Evolutionary Algorithms (MOEAs) are powerful search techniques that have been extensively used to solve difficult problems in a wide variety of disciplines. However, they can be very demanding in terms of computational resources. Parallel implementations of MOEAs (pMOEAs) provide considerable gains regarding performance and scalability and, therefore, their relevance in tackling computationally expensive applications. This paper presents a survey of pMOEAs, describing a refined taxonomy, an up-to-date review of methods and the key contributions to the field. Furthermore, some of the open questions that require further research are also briefly discussed

SALSA: A Formal Hierarchical Optimization Framework for Smart Grid

The smart grid, by the integration of advanced control and optimization technologies, provides the traditional grid with an indisputable opportunity to deliver and utilize the electricity more efficiently. Building smart grid applications is a challenging task, which requires a formal modeling, integration, and validation framework for various smart grid domains. The design flow of such applications must adapt to the grid requirements and ensure the security of supply and demand. This dissertation, by proposing a formal framework for customers and operations domains in the smart grid, aims at delivering a smooth way for: i) formalizing their interactions and functionalities, ii) upgrading their components independently, and iii) evaluating their performance quantitatively and qualitatively.The framework follows an event-driven demand response program taking no historical data and forecasting service into account. A scalable neighborhood of prosumers (inside the customers domain), which are equipped with smart appliances, photovoltaics, and battery energy storage systems, are considered. They individually schedule their appliances and sell/purchase their surplus/demand to/from the grid with the purposes of maximizing their comfort and profit at each instant of time. To orchestrate such trade relations, a bilateral multi-issue negotiation approach between a virtual power plant (on behalf of prosumers) and an aggregator (inside the operations domain) in a non-cooperative environment is employed. The aggregator, with the objectives of maximizing its profit and minimizing the grid purchase, intends to match prosumers' supply with demand. As a result, this framework particularly addresses the challenges of: i) scalable and hierarchical load demand scheduling, and ii) the match between the large penetration of renewable energy sources being produced and consumed. It is comprised of two generic multi-objective mixed integer nonlinear programming models for prosumers and the aggregator. These models support different scheduling mechanisms and electricity consumption threshold policies.The effectiveness of the framework is evaluated through various case studies based on economic and environmental assessment metrics. An interactive web service for the framework has also been developed and demonstrated

Time-energy Analysis of Multilevel Parallelism in Heterogeneous Clusters: the Case of EEG Classification in BCI Tasks

Present heterogeneous architectures interconnect nodes including multiple multi-core microprocessors and accelerators that allow different strategies to accelerate the applications and optimize their energy consumption according to the specific power-performance trade-offs. In this paper, a multi-level parallel procedure is proposed to take advantage of all nodes of a heterogeneous CPU-GPU cluster. Two more alternatives have been implemented, and experimentally compared and analyzed from both running time and energy consumption. Although the paper considers an evolutionary master-worker algorithm for feature selection in EEG classification, the conclusions from the experimental analysis here provided can be frequently applied, as many other useful bioinformatics and data mining applications show the same master-worker profile than the classification problem here considered. Our parallel approach allows to reduce the time by a factor of up to 83, with only about a 4.9% of energy consumed by the sequential procedure, in a cluster with 36 CPU cores and 43 GPU compute units.Spanish Ministerio de Ciencia, Innovación y Universidades under grant PGC2018-098813-B-C31ERDF fun

Evolutionary Dynamic Multi-Objective Optimisation : A survey

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