Parallel Evolutionary Computation Research Articles

Partitional Clustering-Hybridized Neuro-Fuzzy Classification Evolved through Parallel Evolutionary Computing and Applied to Energy Decomposition for Demand-Side Management in a Smart Home

The key advantage of smart meters over rotating-disc meters is their ability to transmit electric energy consumption data to power utilities’ remote data centers. Besides enabling the automated collection of consumers’ electric energy consumption data for billing purposes, data gathered by smart meters and analyzed through Artificial Intelligence (AI) make the realization of consumer-centric use cases possible. A smart meter installed in a domestic sector of an electrical grid and used for the realization of consumer-centric use cases is located at the entry point of a household/building’s electrical grid connection and can gather composite/circuit-level electric energy consumption data. However, it is not able to decompose its measured circuit-level electric energy consumption into appliance-level electric energy consumption. In this research, we present an AI model, a neuro-fuzzy classifier integrated with partitional clustering and metaheuristically optimized through parallel-computing-accelerated evolutionary computing, that performs energy decomposition on smart meter data in residential demand-side management, where a publicly available UK-DALE (UK Domestic Appliance-Level Electricity) dataset is used to experimentally test the presented model to classify the On/Off status of monitored electrical appliances. As shown in this research, the presented AI model is effective at providing energy decomposition for domestic consumers. Further, energy decomposition can be provided for industrial as well as commercial consumers.

Artificial Intelligence, Accelerated in Parallel Computing and Applied to Nonintrusive Appliance Load Monitoring for Residential Demand-Side Management in a Smart Grid: A Comparative Study

A smart grid is a promising use-case of AIoT (AI (artificial intelligence) across IoT (internet of things)) that enables bidirectional communication among utilities that arises with demand response (DR) schemes for demand-side management (DSM) and consumers that manage their power demands according to received DR signals. Disaggregating composite electric energy consumption data from a single minimal set of plug-panel current and voltage sensors installed at the electric panel in a practical field of interest, nonintrusive appliance load monitoring (NIALM), a cost-effective load disaggregation approach for (residential) DSM, is able to discern individual electrical appliances concerned without accessing each of them by individual plug-load power meters (smart plugs) deployed intrusively. The most common load disaggregation approaches are based on machine learning algorithms such as artificial neural networks, while approaches based on evolutionary computing, metaheuristic algorithms considered as global optimization and search techniques, have recently caught the attention of researchers. This paper presents a genetic algorithm, developed in consideration of parallel evolutionary computing, and aims to address NIALM, whereby load disaggregation from composite electric energy consumption data is declared as a combinatorial optimization problem and is solved by the algorithm. The algorithm is accelerated in parallel, as it would involve large amounts of NIALM data disaggregated through evolutionary computing, chromosomes, and/or evolutionary cycles to dominate its performance in load disaggregation and excessively cost its execution time. Moreover, the evolutionary computing implementation based on parallel computing, a feed-forward, multilayer artificial neural network that can learn from training data across all available workers of a parallel pool on a machine (in parallel computing) addresses the same NIALM/load disaggregation. Where, a comparative study is made in this paper. The presented methodology is experimentally validated by and applied on a publicly available reference dataset.

Parallel evolutionary computation for multiobjective gene interaction analysis

Multiple studies provide evidence on the impact of certain gene interactions in the occurrence of diseases. Due to the complexity of genotype–phenotype relationships, it is required the development of highly efficient algorithmic strategies that successfully identify high-order interactions attending to different evaluation criteria. This work investigates parallel evolutionary computation approaches for multiobjective gene interaction analysis. A multiobjective genetic algorithm, with novel optimized design features, is developed and parallelized under problem-independent and problem-dependent schemes. Experimental results show the relevant performance of the method for complex interaction orders, significantly accelerating execution time (up to 296×) with regard to other state-of-the-art multiobjective tools.

PEAR: a massively parallel evolutionary computation approach for political redistricting optimization and analysis

Abstract Political redistricting, a well-known problem in political science and geographic information science, can be formulated as a combinatorial optimization problem, with objectives and constraints defined to meet legal requirements. The formulated optimization problem is NP-hard. We develop a scalable evolutionary computational approach utilizing massively parallel high performance computing for political redistricting optimization and analysis at fine levels of granularity. Our computational approach is based in strong substantive knowledge and deep adherence to Supreme Court mandates. Since the spatial configuration plays a critical role in the effectiveness and numerical efficiency of redistricting algorithms, we have designed spatial evolutionary algorithm (EA) operators that incorporate spatial characteristics and effectively search the solution space. Our parallelization of the algorithm further harnesses massive parallel computing power provided by supercomputers via the coupling of EA search processes and a highly scalable message passing model that maximizes the overlapping of computing and communication at runtime. Experimental results demonstrate desirable effectiveness and scalability of our approach (up to 131K processors) for solving large redistricting problems, which enables substantive research into the relationship between democratic ideals and phenomena such as partisan gerrymandering.

Asynchronous and implicitly parallel evolutionary computation models

This paper presents the design and the application of asynchronous models of parallel evolutionary algorithms. An overview of the existing parallel evolutionary algorithm (PEA) models and available implementations is given. We present new PEA models in the form of asynchronous algorithms and implicit parallelization, as well as experimental data on their efficiency. The paper also discusses the definition of speedup in PEAs and proposes an appropriate speedup measurement procedure. The described parallel EA algorithms are tested on problems with varying degrees of computational complexity. The results show good efficiency of asynchronous and implicit models compared to existing parallel algorithms.

Parallel evolutionary computation in bioinformatics applications

A large number of optimization problems within the field of Bioinformatics require methods able to handle its inherent complexity (e.g. NP-hard problems) and also demand increased computational efforts. In this context, the use of parallel architectures is a necessity.In this work, we propose ParJECoLi, a Java based library that offers a large set of metaheuristic methods (such as Evolutionary Algorithms) and also addresses the issue of its efficient execution on a wide range of parallel architectures. The proposed approach focuses on the easiness of use, making the adaptation to distinct parallel environments (multicore, cluster, grid) transparent to the user. Indeed, this work shows how the development of the optimization library can proceed independently of its adaptation for several architectures, making use of Aspect-Oriented Programming. The pluggable nature of parallelism related modules allows the user to easily configure its environment, adding parallelism modules to the base source code when needed.The performance of the platform is validated with two case studies within biological model optimization.

Distributing evolutionary computation in a networked multi-agent environment

It has become increasingly popular to employ evolutionary algorithms to solve problems in different domains, and parallel models have been widely used for performance enhancement. Instead of using parallel computing facilities or public computing systems to speed up the computation, we propose to implement parallel evolutionary computation models on networked personal computers (PCs) that are locally available and manageable. To realize the parallelism, a multi-agent system is presented in which mobile agents play the major roles to carry the code and move from machine to machine to complete the computation dynamically. To evaluate the proposed approach, we use our multi-agent system to solve two types of time-consuming applications. Different kinds of experiments were conducted to assess the developed system, and the preliminary results show its promise and efficiency.

군집행동 알고리즘을 이용한 판넬구조물의 방사소음저감에 관한 연구

In this paper, the author proposes a new method for acoustic radiation optimum design to minimize noise from a vibrating panel-like structure using a collaborative population-based search method called the particle swarm optimization algorithm(PSOA). The PSOA is a parallel evolutionary computation technique initially developed by Kennedy and Eberhart. The acoustic radiation optimization method based on the PSOA consists of two processes. In the first process, the acoustic radiation analysis by an integrated p-version FEM/BEM, which was developed by using MATLAB, is performed to evaluate the exterior acoustic radiation field of the panel. The second process is to search the optimum design variables: 1) Shape of Bezier curves and 2) Shape and position of ribs, to minimize noise from the panel using the PSOA. The optimization method based on the PSOA is compared to that based on the steady state genetic algorithm(SSGA) in order to verify the effectiveness and validity of the optimal solution by PSOA. Finally, it is shown that the optimal designs of the panel obtained by using the PSOA can achieve effective reductions in radiated sound power.

Parallelizing evolutionary computation: A mobile agent-based approach

Abstract Parallel computation models have been widely used to enhance the performance of traditional evolutionary algorithms, and they have been implemented on parallel computers to speed up the computation. Instead of using expensive parallel computing facilities, we propose to implement parallel evolutionary computation models on easily available networked PCs, and present a multi-agent framework to support parallelism. With the unique characteristics of agent autonomy and mobility, mobile agents can carry the EC-code and migrate from machine to machine to complete the computation dynamically. To evaluate the proposed approach we have developed a prototype system on a middleware platform JADE to solve a time-consuming task. Different kinds of experiments have been conducted to assess the developed system and the preliminary results show the promise and efficiency of our mobile agent-based approach.

超並列PCクラスタの構築と進化的最適化計算

超並列PCクラスタの構築と進化的最適化計算

Acoustic Radiation Optimization Using the Particle Swarm Optimization Algorithm

The present paper describes a fundamental study on structural bending design to reduce noise using a new evolutionary population-based heuristic algorithm called the particle swarm optimization algorithm (PSOA). The particle swarm optimization algorithm is a parallel evolutionary computation technique proposed by Kennedy and Eberhart in 1995. This algorithm is based on the social behavior models for bird flocking, fish schooling and other models investigated by zoologists. Optimal structural design problems to reduce noise are highly nonlinear, so that most conventional methods are difficult to apply. The present paper investigates the applicability of PSOA to such problems. Optimal bending design of a vibrating plate using PSOA is performed in order to minimize noise radiation. PSOA can be effectively applied to such nonlinear acoustic radiation optimization.

PARALLEL AND DISTRIBUTED EVOLUTIONARY COMPUTATION FOR FINANCIAL APPLICATIONS

A survey of two parallel evolutionary computation techniques is presented: the genetic algorithms and genetic programming methods. An application of this approach to the induction of trading models is presented for financial assets, which is known as a hard problem. This study analyses the potential of this approach and the benefit of parallelization.

Evolving processes and evolution schedulers for concurrent scheduling controls and parallel evolutionary computation

In this paper we present concepts of evolution scheduling, which will provide us with a better scheduling mechanism for competing processes in the ready-queue, the blocked-queue, the pc (priority control) queue, and the cs (client-and-server) queue. The evolution scheduling is acceptable for the soft real-time system and useful for its performance to achieve fair and effective scheduling. On the other hand, for those who pursue evolutionary computation, this is good news. It allows them to carry out natural competition in an easy way consistent with their operating system and programming language.

Task Allocation by Parallel Evolutionary Computing

In this paper we will investigate the applicability of parallel evolutionary algorithms to the task allocation problem—a long standing problem in parallel computing. Three different evolutionary optimization strategies, genetic algorithms, simulated annealing, and steepest descent, are formulated in a parallel generic framework. In order to enhance the performance of the strategies, a number of adjustments that exploit problem specific knowledge is proposed. We adopt a parametric description of static parallel applications. As a consequence, a theoretical analysis of the task allocation solution space can be conducted with a method originating from computational biology. The prediction following from this analysis, i.e., simulated annealing performs optimally on the solution space, is supported by experimental results.

Parallel Evolutionary Computing with the Random PROLOG Processor

The Random PROLOG Processor (RPP) is an abstract model for logical computations based on the concept ofinformation_moleculesquasi-randomly traveling and inferring in abstractcomputational_space. The simulated process or problem is defined in terms of PROLOG clauses (or sets of clauses encapsulated by membranes) seeded into the RPP by the programmer as a program. During the computational process,information_moleculesmove about, rendezvous, exchange internal information, modify themselves, assert otherinformation_molecules(or are retracted), disintegrate, or expire. Computations are unification-based logical inferences. In the RPP, the computations are parallel, randomly evolving processes. The RPP exhibits some properties of the DNA-computer, but can be considered for any computational environment. The RPP is proposed as a promising tool for parallel evolutionary computing, because PROLOG is a widely known, powerful language; because prices of multiprocessor architectures are falling sharply; and because parallel RPP implementation is easy. Since they are declarative and nondeterministic, RPP programs are extremely short. This paper presents the structure of the RPP application for parallel Evolutionary Computations with different types of problems.