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HMOAM: hybrid multi-objective genetic optimization for facial analysis by appearance model

Memetic Computing 2010年第1期2卷 25-46页

作者： Sattar, Abdul Seguier, Renaud SUPELEC/IETR 35576 Cesson Sevigne Avenue de la Boulaie France

Successful hybridization of single-objective evolutionary algorithm with gradient based methods has shown promising results. However, studies of hybridized multi-objective Evolutionary Algorithm are limited, especially in the domain of image analysis. This paper presents a novel methodology of hybridization of multi-objective genetic algorithm for the real world optimization problem of facial analysis of multiple camera images by 2. 5D Appearance Model. Facial large lateral movements make acquisition and analysis of facial images by single camera inefficient. Moreover, non-convex multi-dimensional search space formed by the face search by appearance model requires an efficient optimization methodology. Currently, with wide availability of inexpensive cameras, a multi-view system is as practical as a single-view system. To manage these multiple informations, multi-objective genetic algorithm is employed to optimize the face search. To efficiently tackle the problem of non-convexity of the search space, hybridization of NSGA-II (Non-dominated Sorting Genetic Algorithm) with Gradient Descent is proposed in this paper. For this hybridization, we propose a gradient operator in NSGA-II, which computes gradients of the solutions in conjunction with the existing operator of mutation. Thus, it does not increase the computational cost of the system. Another proposition includes a unique method of calculating the relevant information of each camera in a multiple camera system which makes the hybridization procedure efficient and robust. Our proposed algorithm is applied on different facial poses of CMU-PIE database, webcam face images and synthetic face images, and the results are compared with a single view system and a non-hybrid multiple camera system. Simulation results validate the efficiency, accuracy and robustness achieved. © 2010 Springer-Verlag.

关键词： Active appearance model Facial analysis hybrid multi-objective optimization multiple cameras

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A new multi-objective hybrid optimization algorithm for wind-thermal dynamic economic emission power dispatch

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第8期31卷 p1-24页

作者： Xia, Aiming Wu, Xuedong Bai, Yingjie Jiangsu Univ Sci & Technol Sch Elect & Informat Zhenjiang 212003 Jiangsu Peoples R China

This article presents a new optimization method to solve dynamic economic emission dispatch (DEED) problem incorporating wind power by using a hybrid nature inspired multi-objective algorithm based on equilibrium optimizer (EO) and differential evolution (DE). In the proposed algorithm, the EO with a competitive mechanism and an additional exploration strategy is devised to explore the whole search space, while the DE with a ranking mutation operator and an opposition-based learning strategy (OBL) is suggested to evolve the individuals of the external archive. The Kent chaotic map is adopted to generate a uniformly distributed initial population. The approach based on non-dominated sort and improved crowding distance is utilized to screen equilibrium particles' leaders and to update the external archive. These strategies attempt to obtain a Pareto optimal front with excellent diversity and good convergence. Moreover, a real-time constraints adjustment method and a penalty function method are combined to deal with complex constraints. The simulation results on the test system containing 10 thermal power units and one wind farm indicate that the proposed approach has much better performance than other methods for comparison.

关键词： constraint handling technique dynamic economic emission dispatch hybrid multi-objective optimization wind-thermal power

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A hybrid Adaptive Evolutionary Algorithm in the Domination-based and Decomposition-based Frameworks of multi-objective optimization

A Hybrid Adaptive Evolutionary Algorithm in the Domination-b...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Shim, V. A. Tan, K. C. Tan, K. K. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

ISBN: (纸本)9781467315098

Under the framework of evolutionary paradigms, many variations of evolutionary algorithms have been designed. Each of the algorithms performs well in certain cases and none of them are dominating one another. This study is based on the idea of synthesizing different evolutionary algorithms so as to complement the limitations of each algorithm. On top of this idea, this paper proposes an adaptive mechanism that synthesizes a genetic algorithm, differential evolution and estimation of distribution algorithm. The adaptive mechanism takes into account the ratio of the number of promising solutions generated from each optimizer in an early stage of evolutions so as to determine the proportion of the number of solutions to be produced by each optimizer in the next generation. Furthermore, the adaptive algorithm is also hybridized with the evolutionary gradient search to further enhance its search ability. The proposed hybrid adaptive algorithm is developed in the domination-based and decomposition-based multi-objective frameworks. An extensive experimental study is carried out to test the performances of the proposed algorithms in 38 state-of-the-art benchmark test instances.

关键词： Decomposition differential evolution domination genetic algorithm estimation of distribution algorithm evolutionary gradient search hybrid multi-objective optimization

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A hybrid Estimation of Distribution Algorithm for Solving the multi-objective multiple Traveling Salesman Problem

A Hybrid Estimation of Distribution Algorithm for Solving th...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Shim, V. A. Tan, K. C. Tan, K. K. Natl Univ Singapore Dept Elect & Comp Engn Singapore 117548 Singapore

ISBN: (纸本)9781467315098

The multi-objective multiple traveling salesman problem (MmTSP) is a generalization of the classical multi-objective traveling salesman problem. In this paper, a formulation of the MmTSP, which considers the weighted sum of the total traveling costs of all salesmen and the highest traveling cost of any single salesman, is proposed. An estimation of distribution algorithm (EDA) based on restricted Boltzmann machine is used for solving the formulated problem. The EDA is developed in the decomposition framework of multi-objective optimization. Due to the limitation of EDAs in generating a wide range of solutions, the EDA is hybridized with the evolutionary gradient search. Simulation studies are carried out to examine the optimization performances of the proposed algorithm on MmTSP with different number of objective functions, salesmen and problem sizes.

关键词： Decomposition estimation of distribution algorithm evolutionary gradient search hybrid multi-objective optimization multiple traveling salesman problem restricted Boltzmann machine

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