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A Local Search-based Metaheuristic algorithm framework for the School Bus Routing Problem

ENGINEERING LETTERS 2022年第1期30卷 17页

作者： Hou, Yane Liu, Bingbing Dang, Lanxue He, Wenwen Gu, Wenbo Henan Univ Coll Comp & Informat Engn Kaifeng Henan Peoples R China Henan Univ Henan Key Lab Big Data Anal & Proc Kaifeng Henan Peoples R China

School bus service is divided into many kinds of operation modes, such as single school, single load multiple schools and mixed load multiple schools and so on. When it comes to plan bus routes satisfying various constraints and different objectives, there are many applications of school bus routing problem (SBRP). It is generally recognized that middle and large sacle SBRP applications are resolved by heuristic algorithms, because SBRP is a NP-hard problem. This paper proposes a local search-based metaheuristic algorithm framework for SBRP on the basis of analysis of SBRP problem models. The framework, which is implemented by C#, is made up of basic data structures, operation functions, neighborhood operators, initial solution construction algorithm components and heuristic strategies, etc. The neighborhoodcentered metaheuristic algorithms, such as simulated annealing (SA), iterated local search (ILS), variable neighborhood search (VNS), can be designed based on the framework to solve a certain problem type of SBRP. Using this framework, we can solve three operation modes SBRP with homogeneous or heterogeneous bus fleet. Moreover, the proposed metaheuristic framework also provides the development of metaheuristic for the capacitated vehicle routing problem (CVRP) by reducing or modifying the constraints of SBRP. The experimental results of a set of instances show that the metaheuristic algorithm built based on the proposed framework, can be quickly realized and applied to different SBRP applications. Meanwhile, the designed metaheuristic framework is also very effective and extensible.

关键词： algorithm framework school bus routing problem local search metaheuristic neighborhood-centered general-purpose solver

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A General Intelligent Optimization algorithm Combination framework with Application in Economic Load Dispatch Problems

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ENERGIES 2019年第11期12卷 2175页

作者： Zhang, Jinghua Dong, Ze North China Elect Power Univ Hebei Engn Res Ctr Simulat Optimized Control Powe Baoding 071003 Peoples R China

Recently, a population-based intelligent optimization algorithm research has been combined with multiple algorithms or algorithm components in order to improve the performance and robustness of an optimization algorithm. This paper introduces the idea into real world application. Different from traditional algorithm research, this paper implements this idea as a general framework. The combination of multiple algorithms or algorithm components is regarded as a complex multi-behavior population, and a unified multi-behavior combination model is proposed. A general agent-based algorithm framework is designed to support the model, and various multi-behavior combination algorithms can be customized under the framework. Then, the paper customizes a multi-behavior combination algorithm and applies the algorithm to solve the economic load dispatch problems. The algorithm has been tested with four test systems. The test results prove that the multi-behavior combination idea is meaningful which also indicates the significance of the framework.

关键词： population-based intelligent optimization algorithm multi-behavior combination algorithm framework Economic load dispatch (ELD)

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A Quadratically Approximate framework for Constrained Optimization,Global and Local Convergence

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Acta Mathematica Sinica,English Series 2008年第5期24卷 771-788页

作者： Jin Bao JIAN College of Mathematics and Information Science Guangxi University Nanning 530004 P. R. China

This paper presents a quadratically approximate algorithm framework （QAAF） for solving general constrained optimization problems, which solves, at each iteration, a subproblem with quadratic objective function and quadratic equality together with inequality constraints. The global convergence of the algorithm framework is presented under the Mangasarian-Fromovitz constraint qualification （MFCQ）, and the conditions for superlinear and quadratic convergence of the algorithm framework are given under the MFCQ, the constant rank constraint qualification （CRCQ） as well as the strong second-order sufficiency conditions （SSOSC）. As an incidental result, the definition of an approximate KKT point is brought forward, and the global convergence of a sequence of approximate KKT points is analysed.

关键词： constrained optimization quadratic approximation algorithm framework quadratic constraints global and local convergence

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ABC-X: a generalized, automatically configurable artificial bee colony framework

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SWARM INTELLIGENCE 2017年第1期11卷 1-38页

作者： Aydm, Dogan Yavuz, Guecan Stuetzle, Thomas Dumlupinar Univ Dept Comp Engn TR-43000 Kutahya Turkey ULB CoDE IRIDIA Brussels Belgium

The artificial bee colony (ABC) algorithm is a popular metaheuristic that was originally conceived for tackling continuous function optimization tasks. Over the last decade, a large number of variants of ABC have been proposed, making it by now a well-studied swarm intelligence algorithm. Typically, in a paper on algorithmic variants of ABC algorithms, one or at most two of its algorithmic components are modified. Possible changes include variations on the search equations, the selection of candidate solutions to be explored, or the adoption of features from other algorithmic techniques. In this article, we propose to follow a different direction and to build a generalized ABC algorithm, which we call ABC-X. ABC-X collects algorithmic components available from known ABC algorithms into a common algorithm framework that allows not only to instantiate known ABC variants but, more importantly, also many ABC algorithm variants that have never been explored before in the literature. Automatic algorithm configuration techniques can generate from this template new ABC variants that perform better than known ABC algorithms, even when their numerical parameters are fine-tuned using the same automatic configuration process.

关键词： Artificial bee colony Automatic algorithm configuration algorithm framework Continuous optimization

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The EZopt Optimisation framework

The EZopt Optimisation Framework

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IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Hendtlass, Tim Swinburne Univ Technol Melbourne Vic Australia

ISBN: (纸本)9781728121536

This work demonstrates the advantage that can be obtained by dividing optimisation into two separate activities, one to locate promising areas to search and the other to conduct a local search of such promising areas, and assigning these two activities to different algorithms. Promising areas, once searched, can be marked so that they are not searched again, improving efficiency. A framework is presented that enables an implementation of this approach. In this paper Particle Swarm Optimisation (PSO) is used to find promising areas and Directed Random Search (DRS) is used to perform the local optimisation, but there are many alternate algorithms that could be used. It is demonstrated that this framework is suitable for both static and dynamic problems. Sample results are presented for one problem with a plethora of local optima and a second with irregular and changing optima. The performance on these problems, chosen for their highly varied characteristics, allow the framework's performance on a wide range of problems to be inferred.

关键词： algorithm framework static and dynamic optimisation deceptive optimisation meta heuristics memetic algorithm

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Research on convergence of ACO subset algorithms

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COMPEL-THE INTERNATIONAL JOURNAL FOR COMPUTATION AND MATHEMATICS IN ELECTRICAL AND ELECTRONIC ENGINEERING 2013年第2期32卷 649-660页

作者： Chen, Wenyu Bian, Wangyang Zeng, Ru Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Peoples R China

Purpose - The purpose of this paper is to show that the theoretical proofs of convergence in solution of ant colony optimization (ACO) algorithms have significant values of theory and application. Design/methodology/approach - This paper adapts the basic ACO algorithm framework and proves two important ACO subclass algorithms which are ACObs,rmin and ACObs,,,n Findings - This paper indicates that when the minimums of pheromone trial decay to 0 with the speed of logarithms, it is ensured that algorithms can, at least, get a certain optimal solution. Even if the randomicity and deflection of random algorithms are disturbed infinitesimally, algorithms can obtain optimal solution. Originality/value - This paper focuses on the analysis and proof of the convergence theory of ACO subset algorithm to explore internal mechanism of ACO algorithm.

关键词： Programming and algorithm theory Probabilistic analysis Ant colony optimization Nature of convergence algorithm framework Convergence in value Convergence in solution

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IGAOD: An online design framework for interactive genetic algorithms

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SOFTWAREX 2022年 19卷

作者： Huang, DongBo Xu, Xing Minnan Normal Univ Sch Comp Sci Zhangzhou 363000 Peoples R China Minnan Normal Univ Sch Phys & Informat Engn Zhangzhou 363000 Peoples R China

In order to prompt the theoretical research and application of interactive genetic algorithm (IGA), the framework for IGAOD (Interactive Genetic algorithm Online Design) is developed from the perspective of software reuse and the commonness and individuality of IGA. This framework encapsulates the basic commonality of IGA, integrates some classic evolution operators of IGA, and provides visual application scenarios and data analysis charts to help users quickly build their own IGA systems. In addition, the template design pattern can be used to construct the genetic algorithm library, which encapsulates the commonness of a series of selection, crossover and mutation operators into different abstract base classes. These classes inherit the identical abstract base class, making it easier for the algorithm to maintain and expand. In this article, the use of the frame is introduced with the example of 3D vase shape design and plate pattern design. Therefore, the framework is more suitable for the theoretical and application research of IGA.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://***/licenses/by/4.0/).

关键词： Interactive genetic algorithm Java program language algorithm framework Template design pattern Online design

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Data In, Fact Out: Automated Monitoring of Facts by FactWatcher

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PROCEEDINGS OF THE VLDB ENDOWMENT 2014年第13期7卷 1557-1560页

作者： Hassan, Naeemul Sultana, Afroza Wu, You Zhang, Gensheng Li, Chengkai Yang, Jun Yu, Cong Univ Texas Arlington Arlington TX 76019 USA Duke Univ Durham NC 27706 USA Google Res Santa Cruz CA USA

Towards computational journalism, we present FactWatcher, a system that helps journalists identify data-backed, attention-seizing facts which serve as leads to news stories. FactWatcher discovers three types of facts, including situational facts, one-of-the-few facts, and prominent streaks, through a unified suite of data model, algorithm framework, and fact ranking measure. Given an append only database, upon the arrival of a new tuple, FactWatcher monitors if the tuple triggers any new facts. Its algorithms efficiently search for facts without exhaustively testing all possible ones. Furthermore, FactWatcher provides multiple features in striving for an end-to-end system, including fact ranking, fact-to-statement translation and keyword-based fact search.

关键词： algorithm framework Automated monitoring End to end systems Keyword based Multiple features Ranking measures

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A novel ICA algorithm for two sources

A novel ICA algorithm for two sources

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7th International Conference on Signal Processing

作者： Yang, HW Zhang, HM Wang, BX Informat Engn Univ Zhengzhou 450002 Peoples R China

ISBN: (纸本)7121002159

In the independent component analysis (ICA) algorithms, almost all algebraic methods and some adaptive methods can be expressed as "whitening" and "rotating". However, the whitening is just suitable for the simplest ICA model, it may be necessary to develop new algorithm frameworks, which do not need whitening preprocessing. In this paper, a novel algorithm for 2 sources is proposed, which is fast, stable, and does not require whitening. Simulation results are presented, showing good performance of the new algorithm.

关键词： independent component analysis algebraic method algorithm framework whitening

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Exploration of Metaheuristics through Automatic algorithm Configuration Techniques and algorithmic frameworks

Exploration of Metaheuristics through Automatic Algorithm Co...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Franzin, Alberto Stutzle, Thomas Univ Libre Bruxelles IRIDIA Brussels Belgium

ISBN: (纸本)9781450343237

In this paper(1) we argue that flexible algorithm frameworks can be useful to capture the wide variety of algorithmic components for heuristic algorithms and serve as basic experimental frameworks. One of the utilities is that they can implement the wide variety of different algorithm components and their alternative choices for single stochastic local search methods and we are currently extending existing frameworks in that direction. We exemplify this approach considering the example of Simulated Annealing (SA). In fact, a wide variety of design choices of SA algorithms has been proposed in the literature and algorithm frameworks may (i) simply collect potentially all available choices, (ii) provide a tool for the experimental analysis of specific algorithms and component choices, and (iii) allow the generation of new algorithm variants by combining exisiting components in new ways. We show some limited computational experiments that show the benefit of tuning in this context and the way conclusions on the performance of algorithms are altered in this way.

关键词： Metaheuristics Simulated Annealing algorithm Design Automatic algorithm Configuration QAP algorithm framework

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