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Conference on Image Processing - algorithms and Systems III

作者： Maslov, IV CUNY Grad Ctr Dept Comp Sci New York NY 10016 USA

ISBN: (纸本)0819452017

The paper introduces the approach to the 2D automated content-based object recognition utilizing the hybrid evolutionary algorithm (HEA), self-organizing network (SON), and response analysis. When the object is distorted and spatially misregistered, the recognition system has to solve a nonlinear global search problem, i.e. find simultaneously the global positioning of the object and the parameters of its local distortion. The task is accomplished with the HEA using the operators of selection and recombination for the global search, and the accelerated Downhill simplex method for the local search. The algorithm minimizes the fitness formulated as the normalized least squared difference between the images of the scene and the object, and utilizes image local response. The response adequately captures the dynamics of the image transformation, which makes it particularly well suited for the evolutionary search. The response matrix of the object is evaluated and presented to a SON. The weights are computed during the iterative learning process. The resulting adaptive response map serves as the footprint of the object. The evolutionary procedure identifies potential matches for the object based on the response matrix. The local refining procedure uses the response map to accelerate local search in the vicinity of the potential optimal solution.

关键词： hybrid evolutionary algorithm response analysis self-organizing network object recognition

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A hybrid evolutionary algorithm for the Page Number Minimization Problem

A Hybrid Evolutionary Algorithm for the Page Number Minimiza...

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1st International Conference on Computer Science, Engineering and Information Technology (CCSEIT 2011)

作者： Satsangi, Dharna Srivastava, Kamal Gursaran Dayalbagh Educ Inst Dept Math Agra Uttar Pradesh India

ISBN: (纸本)9783642240423

In this paper we present a hybrid evolutionary algorithm (HEA) for solving the pagenumber problem. In HEA. random depth first search of the graph is used for placing the vertices on the spine and an edge embedding heuristic is designed to distribute the edges on a minimal number of pages. The results show that the algorithm achieves the optimal pagenumber for most of the standard graphs tested by us. HEA performance is also compared with the GA described by Kapoor et al. [1] on select instances of standard and random graphs. It is observed that HEA gives a better solution for most of the instances.

关键词： Pagenumber problem hybrid evolutionary algorithm Depth First Search

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Solving a Real-world Wheat Blending Problem Using a hybrid evolutionary algorithm

Solving a Real-world Wheat Blending Problem Using a Hybrid E...

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IEEE Congress on evolutionary Computation

作者： Li, Xiang Bonyadi, Mohammad Reza Michalewicz, Zbigniew Barone, Luigi Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia SolveIT Software Adelaide SA 5000 Australia

ISBN: (纸本)9781479904549;9781479904532

A novel hybrid algorithm is proposed to solve the Australian wheat blending problem. The major part of the problem can be modeled with a linear programming model but the unique constraints make many existing algorithms fail. The algorithm starts with a heuristic that follows pre-defined rules to reduce the search space. Then the linear-relaxed problem is solved using a standard linear programming algorithm, and the result is used to guide an evolutionary-based algorithm while exploring the infeasible regions. Constraint violations are de-penalised if the same choice is made in the linear-relaxed solution. In fact, a hybrid of an evolutionary algorithm, a heuristic method and a linear programming solver is used in the main loop to improve the solution while maintaining the feasibility. A heuristic based initialization method and a local search based post-tuning method are also incorporated into the algorithm. The proposed algorithm has been tested on real data from past years, from small to large cases. Results show that the algorithm is able to find quality results in all cases and outperforms the existing method in use in terms of both quality and speed.

关键词： real-world application hybrid evolutionary algorithm linear programming guided constraint handling

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Ternary selection based hybrid evolutionary algorithm for maximizing water retention on magic squares

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International Journal of Information Technology (Singapore) 2022年第4期14卷 1755-1761页

作者： Hasan, Mahadi Polash, Md Masbaul Alam Department of Computer Science and Engineering Jagannath University Dhaka Bangladesh School of Computer Science University of Sydney Camperdown Australia

Water retention on magic square is the optimization version of the classical magic square problem that has recently attracted significant considerations from researchers. Different approaches have been used to handle this challenging combinatorial optimization problem. In this paper, we have proposed two methodologies. The first one is an improved constraint based tabu search approach that mitigates all the identified limitations of the state-of-the-art procedure. The second one is a hybrid evolutionary algorithm that comprises a tabu search and a genetic algorithm. In the proposed tabu search approach three new strategies have been presented such as an incremental calculation of the objective function, greediness reduction technique during variable selection and taking care of long-term cycling problem through a similarity checking method. The hybrid evolutionary algorithm incorporates our tabu search approach for mutation, a ternary selection method for parent selection including a one-point crossover to combine two magic squares. Experimental results show that our proposed algorithm performs better than the state-of-the-art approach with respect to solution quality and computation time. Moreover, our empirical study reveals that our proposed ternary selection method performs better than the traditional parent selection strategies. By using this new ternary selection method in our hybrid evolutionary algorithm we are able to improve our previous retention values for magic square of order 8. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： hybrid evolutionary algorithm Magic square Tabu search Ternary selection

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An improved genetic algorithm with variable populationsize and a PSO-GA based hybrid evolutionary algorithm

An improved genetic algorithm with variable populationsize a...

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International Conference on Machine Learning and Cybernetics

作者： Shi, XH Wan, LM Lee, HP Yang, XW Wang, LM Liang, YC Jilin Univ Coll Comp Sci & Technol Changchun 130012 Peoples R China

ISBN: (纸本)0780378652

This paper presents an improved genetic algorithm with variable population-size (VPGA) inspired by the natural features of the variable size of the population. Based on the VPGA and the particle swarm optimization (PSO) algorithms, this paper also proposes a novel hybrid approach called PSO-GA based hybrid evolutionary algorithm (PGBHEA). Simulations show that both VPGA and PGBHEA are effective for the optimization problem.

关键词： genetic algorithm particle swarm optimization hybrid evolutionary algorithm optimization

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A Unified Direct Approach to Image Registration and Object Recognition with a hybrid evolutionary algorithm

A Unified Direct Approach to Image Registration and Object R...

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5th International Conference on Intelligent Computing

作者： Maslov, Igor V. Gertner, Izidor St Petersburg State Polytech Univ Dept Intelligent Comp Technol 29 Polytech Skaya St St Petersburg 195251 Russia CUNY City Coll Dept Comp Sci New York NY 10031 USA

ISBN: (纸本)9783642040191

The paper proposes a unified direct approach to a number of problems arising in image processing. In particular, the areas of image registration, and object or pattern recognition are addressed when the images of interest display significant geometric distortion due to some physical or geometrical conditions. The proposed method performs a direct multi-objective search in image response space for an optimal piece-wise affine transformation of the images using a hybrid evolutionary algorithm. In its most general form, the entire algorithm works in two relatively independent. passes. First, the global search attempts to find the optimal solution for the principal affine transformation. During the second pass, the correction procedure seeks for the optimal piecewise approximation of the actual image transformation using the result of the first pass as the initial approximation.

关键词： hybrid evolutionary algorithm image processing piece-wise approximation multi-objective optimization local search

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A hybrid evolutionary algorithm for Strategic Bidding in Day-Ahead Market with Flexible Demand 5

A Hybrid Evolutionary Algorithm for Strategic Bidding in Day...

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5th International Conference on Green Energy and Applications (ICGEA)

作者： Zhang, Haoyang China Southern Power Grid Energy Dev Res Inst Guangzhou Peoples R China

ISBN: (纸本)9781728177199

Bi-level optimization is a widely used tool for modelling the strategic bidding problems in electricity markets. Traditionally, bi-level optimization problems can be solved after converting them to single-level Mathematical Problems with Equilibrium Constraints (MPEC) by Karush-Kuhn-Tucker (KKT) conditions. However, the non-convex and non-linear operating variables of the generators render KKT conditions and MPEC unavailable in strategic bidding optimization problems. To address this problem, this paper proposes a hybrid evolutionary algorithm to solve the bi-level optimization strategic bidding problem in a day-ahead electricity market with flexible demand by transforming the original lower level mixed-integer non-linear problem (MINLP) into mixed-integer linear problem (MILP). The case study result demonstrates the ability of the proposed method to solve the bi-level optimization problem and find a more profitable bidding strategy compared to the benchmark case with a competitive behaviour.

关键词： hybrid evolutionary algorithm bi-level optimization strategic bidding flexible demand

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Inverse Study of Double-Glazed Solar Collector using hybrid evolutionary algorithm 7

Inverse Study of Double-Glazed Solar Collector using Hybrid ...

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7th International Conference on Contemporary Computing (IC3)

作者： Das, Ranjan Indian Inst Technol Ropar Sch Mech Mat & Energy Engn Rupnagar Punjab India

ISBN: (纸本)9781479951734

This paper presents an application of the hybrid evolutionary-nonlinear programming based optimization algorithm for solving an inverse problem of a double-glazed flat-plate solar collector. For a given configuration, the performance of the solar collector can be represented by heat loss factor. In this work, four parameters such as plate-inner glass spacing, inner-outer glass spacing, thickness of the glass cover and the emissivity of the absorber plate have been estimated by the hybrid algorithm for satisfying a given heating requirement in a double-glazed flat-plate solar collector. hybrid algorithm is used to search different unknowns within a prescribed feasible range of various parameters. Many feasible and nearly unique combinations of the four unknowns are observed to satisfy the same heating requirement, which is confirmed by satisfactory reconstruction of the heat loss factor fields.

关键词： hybrid evolutionary algorithm double-glazed solar collector inverse problem optimization

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Using image local response for efficient image fusion with the hybrid evolutionary algorithm

Using image local response for efficient image fusion with t...

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14th Conference on Automatic Target Recognition

作者： Maslov, IV Gertner, I CUNY Grad Ctr Dept Comp Sci New York NY 10016 USA

ISBN: (纸本)0819453498

Image fusion serves as the basis for automatic target recognition;it maps images of the same scene received from different sensors into a common reference system. A novel fusion method is described that employs image local response and the hybrid evolutionary algorithm (HEA). Given geometric transformation A(V) under parameter vector V (e.g. affine image transformation) of the images subjected to fusion, image local response is defined as image transform R(V) that maps the image onto itself, with the small perturbations of the parameter vector V. Unit variations of the components of the parameter vector V are applied to the image, and the corresponding variations of the least squared differences of the gray levels of the two images (i.e. before and after parameter variation) form the image response matrix. The transform R(V) extracts only the dynamic contents of the image, i.e. the salient features that are most sensitive to geometric transformation A(V). Since R(V) maps the image onto itself, the result of the mapping is largely invariant to the type of the sensor that was used to obtain the image. Once the response matrices are built for all images subjected to fusion, HEA is used to map the images into the common reference system.

关键词： hybrid evolutionary algorithm response analysis image fusion target recognition

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Dynamic Economic Emission Dispatch Using Multi-objective hybrid evolutionary algorithm

Dynamic Economic Emission Dispatch Using Multi-objective Hyb...

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International Conference on Sustainable Energy and Environmental Engineering (ICSEEE 2012)

作者： Zhang, Lei Liu, Jun North China Elect Power Univ Beijing Beijing 100871 Peoples R China

ISBN: (纸本)9783037856345

Dynamic economic emission dispatch (DEED) is an important optimization task for power plants. The problem is a highly constrained multi-objective optimization problem involving conflicting objectives with both equality and inequality constraints. This paper introduces two objective functions of DEED model: the lowest generation cost and the smallest carbon emissions with power balance constraints, unit output constraints and unit ramp rate limits Then the paper presents a multi-objective hybrid evolutionary algorithm (MHEA) to solve the DEED model. The MHEA is a hybrid optimization algorithm based on orthogonal initialization, improved differential operation with migration strategy, parameter adaptive control, multi-objective selection strategy and analytic hierarchy process based fuzzy technique (AFT). Numerical results of one test system demonstrate the capabilities of the proposed approach. Compared with other classical methods, the proposed approach gets better result.

关键词： Dynamic economic emission dispatch Multi-objective optimization hybrid evolutionary algorithm

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