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Reference-lines-steered memetic multi-objective evolutionary algorithm with adaptive termination criterion

MEMETIC COMPUTING 2021年第1期13卷 49-67页

作者： Saikia, Riddhiman Sharma, Deepak Indian Inst Technol Dept Mech Engn Gauhati Assam India

Multi-objective evolutionary algorithms (MOEAs) have been the choice for generating a set of Pareto-optimal (PO) solutions in one run. However, these algorithms sometimes suffer slow and poor convergence toward the PO front. One of the remedies to improve their convergence is to couple global search of MOEAs with local search. However, such coupling brings other implementation challenges, such as what, when, and how many solutions can be chosen for local search with MOEAs? In this paper, these challenges are addressed by developing a local search module that can choose solutions for local search using a set of reference lines. The heuristic strategies are also developed with the module for determining the frequency of executing local search and for terminating MOEA adaptively using a statistical performance indicator. The proposed algorithm, which is referred to as RM(2)OEA, is tested on 2-objective ZDT and 3-objective DTLZ test problems. Results demonstrate faster and improved convergence of RM2 OEA over a benchmark MOEA from the literature.

关键词： Multi-objective optimization hybrid evolutionary algorithm Memetic evolutionary algorithm Reference lines Adaptive termination condition

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High Coverage Point-To-Point Transit: hybrid evolutionary approach to local vehicle routing

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KSCE JOURNAL OF CIVIL ENGINEERING 2015年第6期19卷 1882-1891页

作者： Jung, Jaeyoung Jayakrishnan, R. Nam, Doohee Univ Calif Irvine Inst Transportat Studies Irvine CA 92697 USA Univ Calif Irvine Dept Civil & Environm Engn Irvine CA 92697 USA Hansung Univ Dept Informat Syst Engn Seoul 136792 South Korea

High Coverage Point-to-Point Transit (HCPPT) is a new design of alternative transportation, which involves a sufficient number of deployed small vehicles in real-time response. This paper focuses on a hybrid evolutionary approach to improve the existing local vehicle routing algorithm of HCPPT. A hybrid Genetic algorithm (GA) is proposed by utilizing an insertion heuristic method as a genetic operator. First, two genetic operation schemes, Random Feasible Position (RFP) and Best Feasible Position (BFP), are designed and tested on a simulation framework that we have developed recently. The simulation result shows the effectiveness of BFP in terms of system performance and computational efficiency. Next, we investigate a combined scheme that builds the initial BFP population based on an insertion heuristic. The second simulation reveals that the proposed initial population method provides considerably better solution qualities over the various problem constraints. The simulations in this study are performed with various demand levels based on SCAG (Southern California Association of Governments) transportation network and OCTA (Orange County Transportation Authority) trip demands.

关键词： High Coverage Point-to-point Transit (HCPPT) vehicle routing algorithm real-time Demand Responsive Transit (DRT) Genetic algorithm (GA) hybrid evolutionary algorithm insertion heuristic

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Spatially-explicit modelling and forecasting of cyanobacteria growth in Lake Taihu by evolutionary computation

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ECOLOGICAL MODELLING 2015年 306卷 216-225页

作者： Zhang, Xiaoqing Recknagel, Friedrich Chen, Qiuwen Cao, Hongqing Li, Ruonan Chinese Acad Sci RCEES Beijing 100085 Peoples R China Nanjing Hydraul Res Inst CEER Nanjing 210029 Jiangsu Peoples R China Univ Adelaide Sch Earth & Environm Sci Adelaide SA 5005 Australia

Models are proved to be effective instrument for algae bloom prediction and management. The commonly available prediction models are physically based numerical approach or data-driven approach. However, these models are sometimes restricted by the lack of an explicit representation function or by insufficient data. The present research aimed to develop forecasting models that provide early warning on cyanobacteria outbreaks, as well as understand the ecological thresholds and relationships that determine such events, by means of evolutionary computation. The Lake Taihu, which has been suffering from severe cyanobacteria blooms over the last decades due to eutrophication, was taken as study case. Two modelling approaches were used based on water quality data collected from 31 monitoring sites from 2008 to 2012. First, eight sampling sites representing spatially different environmental conditions across Lake Taihu were selected to develop 2-day ahead forecasting models. The resulting models well-matched the timing and magnitude of the observed cyanobacteria dynamics for all eight sites, which was reflected by coefficients of determination (r(2)) of 0.62 for eastern site 24 being least favourable to cyanobacteria growth and 0.83 for north-western site 6 being most favourable. The sensitivity analyses revealed inhibitory relationships with nitrate at water temperatures greater than 18 degrees C and excitatory relationships with phosphate at lower water temperatures for most sites, which suggested N-limitation of the lake existed locally in summer and autumn. Second, the aggregated data from all 31 sites were used to develop a generic 2-day ahead forecasting model. When compared with the observed cyanobacteria data of the eight selected sampling sites, the generic model achieved slightly lower coefficients of determination than the site-specific models, with the lowest r(2) value for site 24 (0.36) and the highest r(2) value for site 6 (0.77). The sensitivity analysis fo

关键词： Cyanobacteria blooms hybrid evolutionary algorithm Forecasting Thresholds Sensitivity analysis

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MEALS: A multiobjective evolutionary algorithm with local search for solving the bi-objective ring star problem

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2016年第2期250卷 377-388页

作者： Calvete, Herminia I. Gale, Carmen Iranzo, Jose A. Univ Zaragoza IUMA Dept Metod Estadist Pedro Cerbuna 12 E-50009 Zaragoza Spain Univ Zaragoza IUMA Dept Metod Estadist Maria Luna 3 Zaragoza 50018 Spain

In this paper we develop a hybrid metaheuristic for approaching the Pareto front of the bi-objective ring star problem. This problem consists of finding a simple cycle (ring) through a subset of nodes of a network. The aim is to minimize both the cost of connecting the nodes in the ring and the cost of allocating the nodes not in the ring to nodes in the ring. The algorithm preserves the general characteristics of a multiobjective evolutionary algorithm and embeds a local search procedure which deals with multiple objectives. The encoding scheme utilized leads to solving a Traveling Salesman Problem in order to compute the ring associated with the chromosome. This allows the algorithm to implicitly discard feasible solutions which are not efficient. The algorithm also includes an ad-hoc initial population construction which contributes to diversification. Extensive computational experiments using benchmark problems show the performance of the algorithm and reveal the noteworthy contribution of the local search procedure. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.

关键词： Ring star problem hybrid evolutionary algorithm Bi-objective optimization Local search Metaheuristics

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A hybrid evolutionary approach for heterogeneous multiprocessor scheduling

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SOFT COMPUTING 2009年第8-9期13卷 833-846页

作者： Goh, C. K. Teoh, E. J. Tan, K. C. Data Storage Inst Spintron Media & Interface Div Singapore 117608 Singapore Natl Univ Singapore Dept Elect & Comp Engn Singapore 117576 Singapore

This article investigates the assignment of tasks with interdependencies in a heterogeneous multiprocessor environment;specific to this problem, task execution time varies depending on the nature of the tasks as well as with the processing element assigned. The solution to this heterogeneous multiprocessor scheduling problem involves the optimization of complete task assignments and processing order between the assigned processors to arrive at a minimum makespan, subject to a precedence constraint. To solve an NP-hard combinatorial optimization problem, as is typified by this problem, this paper presents a hybrid evolutionary algorithm that incorporates two local search heuristics, which exploit the intrinsic structure of the solution, as well as through the use of specialized genetic operators to promote exploration of the search space. The effectiveness and contribution of the proposed features are subsequently validated on a set of benchmark problems characterized by different degrees of communication times, task, and processor heterogeneities. Preliminary results from simulations demonstrate the effectiveness of the proposed algorithm in finding useful schedule sets based on the set of new benchmark problems.

关键词： Multiprocessor scheduling Heterogeneous hybrid evolutionary algorithm Local search Precedence

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Reliability based generator maintenance scheduling using hybrid evolutionary approach

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2012年第1期42卷 434-439页

作者： Reihani, Ehsan Sarikhani, Ali Davodi, Moez Davodi, Mehdi Asrar Inst Higher Educ Mashhad Iran Florida Int Univ Miami FL 33174 USA

With the growth of electrical energy demand, providing reliable energy without interruption has become very important nowadays. Maintenance scheduling of generating units is one of the crucial factors in delivering reliable electrical energy to the vital industrial and urban loads. As number of generating units and constraints over their operation is increasing, there is growing need for developing new methods for planning optimal outage of generating units for maintenance. This paper presents a hybrid evolutionary algorithm to tackle the reliability based generator maintenance scheduling problem. Uncertainties in the generating units and the load variations are included so that a more realistic scheduling is obtained. Maintenance scheduling problem is a large scale constrained optimization problem with a large number of variables which needs novel methods to cope with it. A new local search method which is derived from Extremal Optimization (EO) and Genetic algorithm (GA) is presented to tackle the problem. The proposed method can be used as a local optimizer to further improve the potential solutions in the GA. The proposed method, Hill Climbing Technique (HCT), GA and their hybrid approaches are applied to the IEEE Reliability Test System (RTS) and the obtained results are discussed. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： Generator maintenance scheduling hybrid evolutionary algorithm Extremal optimization Levelised risk method

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hybrid evolutionary-Based Sparse Channel Estimation for IRS-Assisted mmWave MIMO Systems

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IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 2022年第3期21卷 1586-1601页

作者： Chen, Zhen Tang, Jie Zhang, Xiu Yin So, Daniel Ka Chun Jin, Shi Wong, Kai-Kit South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL Lancs England Southeast Univ Natl Mobile Commun Res Lab Nanjing 210096 Peoples R China UCL Dept Elect & Elect Engn London WC1E 6BT England

The intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication system has emerged as a promising technology for coverage extension and capacity enhancement. Prior works on IRS have mostly assumed perfect channel state information (CSI), which facilitates in deriving the upper-bound performance but is difficult to realize in practice due to passive elements of IRS without signal processing capabilities. In this paper, we propose a compressive channel estimation techniques for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity of mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel is converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multiobjective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed method achieves competitive error performance compared to existing channel estimation methods.

关键词： Channel estimation Wireless communication Optimization Millimeter wave communication Matching pursuit algorithms Massive MIMO Training Intelligent reflecting surface (IRS) millimeter wave communications channel estimation compressed sensing hybrid evolutionary algorithm

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hybrid evolutionary approach for designing neural networks for classification

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ELECTRONICS LETTERS 2004年第15期40卷 955-957页

作者： Tan, ZH Univ Aalborg Dept Commun Technol DK-9220 Aalborg Denmark

An approach for the automatic design of artificial neural networks is presented where a hybrid evolutionary algorithm (HEA) is applied to the structural and parametric learning of networks. The HEA combines genetic algorithms and evolutionary programming on the basis of a real-valued multi-matrix representation. Experimental results show that the proposed approach has a good generalisation and a low computational cost.

关键词： neural nets learning (artificial intelligence) computational cost Neural nets Optimisation techniques artificial neural networks pattern classification automatic design genetic algorithms structural learning parametric learning generalisation Learning in AI Neural computing techniques hybrid evolutionary algorithm multimatrix representation evolutionary programming

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evolutionary approach to human body registration

Evolutionary approach to human body registration

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Conference on Automatic Target Recognition XVI

作者： Maslov, Igor V. Gertner, Izidor CUNY Grad Ctr Dept Comp Sci 365 5th Ave New York NY 10016 USA

ISBN: (纸本)081946290X

Human body registration is an important and complex problem that can be found in a variety of real world applications. Registration maps images of a person obtained from different camera views into a common reference system of a scene that contains a human figure. The complexity of the problem stems from the fact that human body can arbitrarily move in the 3-D space while changing its own shape. The registration task is stated as a nonlinear global multimodal optimization problem, i.e., as a search for a proper transformation that provides the best match between the images of the body and the scene. The paper describes an approach to human body registration that utilizes a hybrid evolutionary algorithm and image response analysis. hybrid evolutionary algorithm provides an efficient procedure of global search in extremely large parameter space. Image response analysis allows to reduce the total amount of information that has to be processed during evaluation of potential solutions. In the process of the evolutionary search, response matrix of each template image is compared against response matrix of the reference image of the scene, in order to find the correct mapping between the images. The efficiency of the proposed approach is demonstrated on a test set of 2-D grayscale images.

关键词： human body registration global optimization hybrid evolutionary algorithm image response analysis

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A Novel Handwritten Letter Recognizer Using Enhanced evolutionary Neural Network

A Novel Handwritten Letter Recognizer Using Enhanced Evoluti...

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2nd International Conference on Forensic Applications and Techniques in Telecommunications, Information and Multimedia

作者： Mahmoudi, Fariborz Mirzashaeri, Mohsen Shahamatnia, Ehsan Faridnia, Saed Islamic Azad Univ Qazvin Branch Elect & Comp Engn Dept Tehran Iran

ISBN: (纸本)9783642023118

This paper introduces a novel design for handwritten letter recognition by employing a hybrid back-propagation neural network with an enhanced evolutionary algorithm. Feeding the neural network consists of a new approach which is invariant to translation, rotation, and scaling of input letters. evolutionary algorithm is used for the global search of the search space and the back-propagation algorithm is used for the local search. The results have been computed by implementing this approach for recognizing 26 English capital letters in the handwritings of different people. The computational results show that the neural network reaches very satisfying results with relatively scarce input data and a promising performance improvement in convergence of the hybrid evolutionary back-propagation algorithms is exhibited.

关键词： Handwritten Character Recognition Neural Network hybrid evolutionary algorithm EANN

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