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hybrid algorithms for complete exchange in 2D meshes

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2001年第12期12卷 1201-1218页

作者： Sundar, NS Jayasimha, DN Panda, DK Sadayappan, P Hewlett Packard Corp Cupertino CA 95014 USA Intel Corp Santa Clara CA 95052 USA Ohio State Univ Dept Comp & Informat Sci Columbus OH 43210 USA

Parallel algorithms for several common problems: such as sorting and the FFT involve a personalized exchange of data among all the processors. Past approaches to doing complete exchange have taken one of two broad approaches: direct exchange or the indirect message-combining approaches. While combining approaches reduce the number of message startups, direct exchange minimizes the volume of data transmitted. This paper presents a family of hybrid algorithms for wormhole-routed 2D meshes that can effectively utilize the complementary strengths of these two approaches to complete exchange. The performance of hybrid algorithms using Cyclic Exchange [26] and Scott's Direct Exchange [23] are studied using analytical models, simulation, and implementation on a Cray T3D system. The results show that hybrids achieve lower completion times than either pure algorithm for a range of mesh sizes, data block sizes, and message startup costs. It is also demonstrated that barriers may be used to enhance performance by reducing message contention, whether or not the target system provides hardware support for barrier synchronization. The analytical models are shown useful in selecting the optimum hybrid for any given combination of system parameters (mesh size, message startup time, flit transfer time, and barrier cost) and the problem parameter (data block: size).

关键词： collective communication complete exchange combining direct exchange hybrid algorithms message contention barrier synchronization mesh topology wormhole routing

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hybrid algorithms for electromagnetic problems and the no-free-lunch framework

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2007年第3期55卷 742-749页

作者： Quevedo-Teruel, Oscar Rajo-Iglesias, Eva Oropesa-Garcia, Alicia Univ Carlos III Madrid Dept Signal Theory & Commun Madrid 28911 Spain

A popular approach to electromagnetic design is based on generic optimization algorithms which mimic natural phenomena. These algorithms have been proved feasible through multiple experiments even though there is no standard procedure to their design for a particular problem. hybrid schemes allow an insightful and quite efficient approach towards their design. Furthermore, an intrinsic characteristic of this class of algorithms is their randomness, which is precisely the base the no-free-lunch theorem exploits to measure their fit to a particular problem. This characterization permits a reproducible comparison between algorithms through their fitness density functions.

关键词： algorithm comparison hybrid algorithms no-free-lunch (NFL) framework

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hybrid algorithms for the twin-screw extrusion configuration problem

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APPLIED SOFT COMPUTING 2014年 23卷 298-307页

作者： Teixeira, Cristina Covas, Jose Stutzle, Thomas Gaspar-Cunha, Antonio Univ Minho Inst Polymer & Composites AIPC I3N Guimaraes Portugal Univ Libre Bruxelles IRIDIA B-1050 Brussels Belgium

The twin-screw configuration problem (TSCP) arises in the context of polymer processing, where twinscrew extruders are used to prepare polymer blends, compounds or composites. The goal of the TSCP is to define the configuration of a screw from a given set of screw elements. The TSCP can be seen as a sequencing problem as the order of the screw elements on the screw axis has to be defined. It is also inherently a multi-objective problem since processing has to optimize various conflicting parameters related to the degree of mixing, shear rate, or mechanical energy input among others. In this article, we develop hybrid algorithms to tackle the bi-objective TSCP. The hybrid algorithms combine different local search procedures, including Pareto local search and two phase local search algorithms, with two different population-based algorithms, namely a multi-objective evolutionary algorithm and a multi-objective ant colony optimization algorithm. The experimental evaluation of these approaches shows that the best hybrid designs, combining Pareto local search with a multi-objective ant colony optimization approach, outperform the best algorithms that have been previously proposed for the TSCP. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Twin-screw extruder Multi-objective optimization hybrid algorithms Ant colony optimization Evolutionary algorithms Pareto local search

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hybrid algorithms for the uncapacitated continuous location-allocation problem

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2008年第1-2期37卷 202-209页

作者： Jabalameli, Mohammad Saeed Ghaderi, Abdolsalam Iran Univ Sci & Technol Tehran Iran

In this paper, hybrid algorithms are developed for the multisource location-allocation problem in continuous space. Three hybrid algorithms are proposed to solve this problem that combine elements of several traditional metaheuristics (genetic algorithm and variable neighborhood search) and local searches to find near-optimal solutions. Many problems from the literature have been solved with these algorithms and the obtained results confirm the robustness of the proposed hybrid algorithms. Moreover, the results show that in comparison to the best methods in literature (GA and VNS), these algorithms provide some better solutions.

关键词： location allocation local search metaheuristic hybrid algorithms

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Advanced Path Planning for UAV Swarms in Smart City Disaster Scenarios Using hybrid Metaheuristic algorithms

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DRONES 2025年第1期9卷 64-64页

作者： Adam, Mohammed Sani Abdullah, Nor Fadzilah Abu-Samah, Asma Amodu, Oluwatosin Ahmed Nordin, Rosdiadee Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Bangi 43600 Malaysia Univ Kebangsaan Malaysia Fac Engn & Built Environm Dept Elect Elect & Syst Engn Wireless Res UKM Bangi 43600 Malaysia Univ Putra Malaysia UPM Dept Commun Technol & Network Serdang 43400 Malaysia Sunway Univ Sch Engn & Technol 5 Jalan Univ Bandar Sunway 47500 Malaysia

In disaster-stricken areas, rapid restoration of communication infrastructure is critical to ensuring effective emergency response and recovery. Swarm UAVs, operating as mobile aerial base stations (MABS), offer a transformative solution for bridging connectivity gaps in environments where the traditional infrastructure has been compromised. This paper presents a novel hybrid path planning approach combining affinity propagation clustering (APC) with genetic algorithms (GA), aimed at maximizing coverage, and ensuring quality of service (QoS) compliance across diverse environmental conditions. Comprehensive simulations conducted in suburban, urban, dense urban, and high-rise urban environments demonstrated the efficacy of the APC-GA approach. The proposed method achieved up to 100% coverage in suburban settings with only eight unmanned aerial vehicle (UAV) swarms, and maintained superior performance in dense and high-rise urban environments, achieving 97% and 93% coverage, respectively, with 10 UAV swarms. The QoS compliance reached 98%, outperforming benchmarks such as GA (94%), PSO (90%), and ACO (88%). The solution exhibited significant stability, maintaining consistently high performance, highlighting its robustness under dynamic disaster scenarios. Mobility model analysis further underscores the adaptability of the proposed approach. The reference point group mobility (RPGM) model consistently achieved higher coverage rates (95%) than the random waypoint model (RWPM) (90%), thereby demonstrating the importance of group-based mobility patterns in enhancing UAV deployment efficiency. The findings reveal that the APC-GA adaptive clustering and path planning mechanisms effectively navigate propagation challenges, interference, and non-line-of-sight (NLOS) conditions, ensuring reliable connectivity in the most demanding environments. This research establishes the APC-GA hybrid as a scalable and QoS-compliant solution for UAV deployment in disaster response scenarios.

关键词： UAV swarms path optimization disaster response hybrid algorithms Affinity Propagation Clustering Genetic Algorithm

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hybrid algorithms in constraint programming

Hybrid algorithms in constraint programming

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11th Annual ERCIM International Workshop on Constraint Solving and Contraint Logic Programming

作者： Wallace, Mark Monash Univ Fac Informat Technol Clayton Vic 3800 Australia

This paper surveys hybrid algorithms from a constraint programming perspective. It introduces techniques used within a constructive search framework, such as propagation and linear relaxation, as well as techniques us... 详细信息

ISBN: (纸本)9783540738169

关键词： constraint programming hybrid algorithms search

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hybrid algorithms Based on Harmony Search and Differential Evolution for Global Optimization 09

Hybrid Algorithms Based on Harmony Search and Differential E...

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World Summit on Genetic and Evolutionary Computation (GEC 09)

作者： Li, Ling-po Wang, Ling Tsinghua Univ TNList Dept Automat Beijing 100084 Peoples R China

ISBN: (纸本)9781605583266

In this paper, two hybrid algorithms are proposed for global optimization by merging the mechanisms of Harmony Search (HS) and Differential Evolution (DE). First, the learning mechanism of a variant of HS named Global-best Harmony Search (GHS) is embedded into the framework of DE to develop an algorithm called Global Harmony Differential Evolution (GHDE). Besides, the differential operator of DE is introduced into the framework of GHS to develop another new algorithm called Differential Harmony Search (DHS). Numerical simulations are carried out based a set of benchmarks. And simulation results and comparisons show that the hybrid algorithms are superior to the GHS and DE in terms of searching efficiency and searching quality. Meanwhile, the effect of some key parameters on the performances of DHS is investigated.

关键词： Global-best Harmony Searching Differential Evolutionary hybrid algorithms Global Optimization

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Optimal Design of Magnetorheological Damper Based on Tuning Bouc-Wen Model Parameters Using hybrid algorithms

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KSCE JOURNAL OF CIVIL ENGINEERING 2020年第3期24卷 867-878页

作者： Azar, Bahman Farahmand Veladi, Hedayat Talatahari, Siamak Raeesi, Farzad Univ Tabriz Dept Civil Engn Tabriz Iran Near East Univ Dept Engn TR-99138 Nicosia Turkey

This paper presents a useful approach to optimally design magnetorheological (MR) dampers used in structural buildings. To fulfill this aim, damper parameters are regarded as the design variables whose values can be obtained through an optimization process. To improve the quality of searching for the optimum parameters of MR dampers, charged system search (CSS) and grey wolf (GW) algorithms, two of the most widely utilized meta-heuristic algorithms, are used together, and hybrid CSS-GW is presented. To show the authenticity and robustness of the new algorithm in solving optimization problems, some benchmark test functions are tested, at first. Then, an eleven-story benchmark building equipped with 3 MR dampers is considered to get the optimum design of the MR damper using the hybrid CSS-GW. Results show that the developed hybrid algorithm can successfully figure out the optimum parameters of the MR dampers.

关键词： Optimum design Magnetorheological damper Optimum parameters Grey wolf algorithm Charged system search algorithm hybrid algorithms

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Using a bank of binary Hopfield networks as constraints solver in hybrid algorithms

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NEUROCOMPUTING 2008年第4-6期71卷 1061-1068页

作者： Salcedo-Sanz, Sancho Ortiz-Garcia, Emilio G. Perez-Bellido, Angel M. Portilla-Figueras, Jose A. Univ Alcala de Henares Escuela Politecn Super Dept Signal Theory & Commun Madrid 28871 Spain

This paper proposes the use of a bank of Hopfield networks to solve a class of constraints which appear in combinatorial optimization problems. Specifically, we deal with problems which constraints' structure can be represented by a binary matrix C, and can be separated in independent substructures. We show that a bank of Hopfield networks can solve these constraints, and also can be easily hybridized with a global search algorithm, such as simulated annealing, to obtain a final solution to the problem. We apply our approach to the solution of a famous logic-type puzzle, the light-up puzzle, where we report improvements over a branch and bound algorithm, and to an important problem which arises in electronic control: the so-called Crossbar Switch Problem. (c) 2007 Elsevier B.V. All rights reserved.

关键词： bank of Hopfield networks hybrid algorithms combinatorial optimization problems light-up puzzle crossbar switch problem

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Optimization of network planning by the novel hybrid algorithms of intelligent optimization techniques

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ENERGY 2009年第10期34卷 1539-1551页

作者： Sadegheih, A. Univ Yazd Dept Ind Engn Yazd Iran

This paper proposes a new hybrid algorithm Meta-heuristic for the problem of network planning systems. The main goal of this paper is, to develop an efficient optimization tool which will minimise the cost functions of the stated optimization problems in network planning systems. The following are the objectives of the research: to investigate the capabilities of genetic algorithm, simulated annealing and tabu search for the defined optimization tasks;to develop a hybrid optimization algorithm which will produce improved iterations compared to those found by GA, SA, and TS algorithms. The performance of the hybrid algorithm is illustrated and six hybrid algorithms are developed, to improve the iterations obtained. The cost function of this problem consists of the capital investment cost in discrete form, the cost of transmission losses and the power generation costs. It is advantageous to use exact DC load flow constraint equations based on the modified form of Kirchhoff's Second Law because the iterative process for line addition is not required. Hence, the computation time is decreased. Finally, the hybrid VI shows to be a very good option for network planning systems given that it obtains much accentuated reductions of iteration, which is very important for network planning. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： hybrid algorithms Evolutionary computation techniques System planning Simulated annealing Genetic algorithm Tabu search Mathematical programming Heuristic techniques

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