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HBBA: hybrid algorithm for buffer allocation in tandem production lines

JOURNAL OF INTELLIGENT MANUFACTURING 2007年第3期18卷 411-420页

作者： Dolgui, Alexandre Eremeev, Anton V. Sigaev, Viatcheslav S. Ecole Natl Super Mines Div Ind Engn & Comp Sci F-42023 St Etienne 2 France Sobolev Inst Math Omsk Branch Discrete Optimizat Lab Omsk Russia Omsk State Univ Dept Math Omsk 644077 Russia

In this paper, we consider the problem of buffer space allocation for a tandem production line with unreliable machines. This problem has various formulations all aiming to answer the question: how much buffer storage to allocate between the processing stations? Many authors use the knapsack-type formulation of this problem. We investigate the problem with a broader statement. The criterion depends on the average steady-state production rate of the line and the buffer equipment acquisition cost. We evaluate black-box complexity of this problem and propose a hybrid optimization algorithm (HBBA), combining the genetic and branch-and-bound approaches. HBBA is excellent in computational time. HBBA uses a Markov model aggregation technique for goal function evaluation. Nevertheless, HBBA is more general and can be used with other production rate evaluation techniques.

关键词： production line buffer allocation NP completeness black-box complexity genetic algorithm branch-and-bound method hybrid algorithm

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hybrid algorithm Based on Phasor Particle Swarm Optimization and Bacterial Foraging Optimization 14th

Hybrid Algorithm Based on Phasor Particle Swarm Optimization...

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14th International Conference on Swarm Intelligence (ICSI)

作者： Liu, Xiaole Wu, Chenhan Chen, Peilin Wang, Yongjin Coll Int Educ Shenzhen 518048 Peoples R China Whittle Sch & Studios Shenzhen Campus Shenzhen 518067 Peoples R China Shezhen Univ Greater Bay Area Int Inst Innovat Shenzhen 518060 Peoples R China

ISBN: (纸本)9783031366215;9783031366222

In response to the issues of premature convergence and instability of the phasor particle swarm optimization (PPSO) for solving function optimization problems, a new hybrid algorithm called bacteria PPSO (BPPSO) was proposed which combines the chemotaxis operation of the bacterial foraging optimization (BFO) algorithm with PPSO. In BPPSO, all individuals undergo tumbling and swimming strategies when the chemotaxis condition is met. New coefficients are introduced to update the positions of particles in BPPSO, achieving complementary advantages of BFO and PPSO. Finally, BPPSO is validated using eight benchmark functions, demonstrating its fast convergence speed, high computational accuracy, and good stability, making it a powerful global optimization algorithm.

关键词： Phasor particle swarm optimization Bacteria foraging optimization hybrid algorithm Chemotaxis

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Profit maximization of TSP through a hybrid algorithm

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COMPUTERS & INDUSTRIAL ENGINEERING 2015年 88卷 229-236页

作者： Khanra, Aditi Maiti, Manas Kumar Maiti, Manoranjan Bohichberia High Sch HS Purba Medinipur 721649 W Bengal India Mahishadal Raj Coll Dept Math Purba Medinipur 721628 WB India Vidyasagar Univ Dept Appl Math Oceanol & Comp Programming Paschim Medinipur WB India

Here a new model of Traveling Salesman Problem (TSP) with uncertain parameters is formulated and solved using a hybrid algorithm. For this TSP, there are some fixed number of cities and the costs and time durations for traveling from one city to another are known. Here a Traveling Salesman (TS) visits and spends some time in each city for selling the company's product. The return and expenditure at each city are dependent on the time spent by the TS at that city and these are given in functional forms of t. The total time limit for the entire tour is fixed and known. Now, the problem for the TS is to identify a tour program and also to determine the stay time at each city so that total profit out of the system is maximum. Here the model is solved by a hybrid method combining the Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The problem is divided into two subproblems where ACO and PSO are used successively iteratively in a generation using one's result for the other. Numerical experiments are performed to illustrate the models. Some behavioral studies of the models and convergences of the proposed hybrid algorithm with respect to iteration numbers and cost matrix sizes are presented. (C) 2015 Published by Elsevier Ltd.

关键词： Ant colony optimization Particle swarm optimization Travel cost Travel time Profit hybrid algorithm

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A hybrid algorithm of ABC variant and enhanced EGS local search technique for enhanced optimization performance

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2018年 74卷 10-22页

作者： Sulaiman, Noorazliza Mohamad-Saleh, Junita Abro, Abdul Ghani Univ Sains Malaysia Sch Elect & Elect Engn Nibong Tebal Penang Malaysia King Saud Univ Coll Appl Engn Al Muzahimiyah Campus Riyadh Saudi Arabia

A hybrid algorithm for optimizing a complex power system-based problem, economic environmental dispatch (EED) is proposed. The algorithm hybridizes a recently proposed artificial bee colony (ABC) variant referred to as JA-ABC3 and a local search technique, evolutionary gradient search (EGS) which has been enhanced i.e., augmented. The enhanced EGS has been inserted into JA-ABC3's framework and the resulting hybrid algorithm, known as EGSJAABC3 is expected to exhibit robust optimization performance by showing the capability to reach the global optimum in less number of generations. JA-ABC3 which is generated through few modifications towards the standard ABC algorithm is the best candidate as it has exhibited better performance than the standard ABC and other ABC variants. Since JA-ABC3 is a global search algorithm, a local search technique, EGS that has been augmented is selected to be its hybrid partner as it also exhibits better or same performance than its kind. The task of the augmented EGS is to enhance the exploitation capability of the algorithm and thus, guides the solution faster towards the global optimum. In other word, the enhanced EGS is taking part in the exploitation process while JA-ABC3 takes role in exploration and some parts of the exploitation processes. Then, a number of benchmark functions are used to evaluate the robustness of EGSJAABC3 in terms of convergence speed and global optimum achievement. Next, the main significant output of this research is a robust optimization algorithm (i.e., EGSJAABC3) later applied to solve complex real-world problems that is known for their uncertainty. In this paper, EGSJAABC3 is tested to minimize EED on three test generator systems;6, 10 and 40 units. The acquired outcome on both benchmark functions and EED application demonstrate the robustness of EGSJAABC3 as an optimization algorithm and therefore, provide other researchers and engineers a tool for solving optimization problems.

关键词： Artificial bee colony Benchmark functions Convergence speed Economic environmental dispatch Global optimum hybrid algorithm

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Optimizing an emission trading scheme for local governments: A Stackelberg game model and hybrid algorithm

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2017年 193卷 172-182页

作者： Hong, Zhaofu Chu, Chengbin Zhang, Linda L. Yu, Yugang Lanzhou Univ Sch Management Lanzhou Gansu Peoples R China Univ Paris Saclay Cent Supelec Lab Genie Ind Chatenay Malabry France Catholic Univ Lille IESEG Sch Management LEM CNRS Lille France Univ Sci & Technol China Sch Management Hefei Anhui Peoples R China

This study investigates a policy-making problem for a local government to implement an emission trading scheme by considering the interactive production decisions of firms in its administrative region. The market-based allowance trading price formed freely among the firms in the region is investigated by taking into account regional environmental bearing capacities. Under the scheme, the government sets the emission reduction target of the region and allocates tradable initial allowances to firms, and firms plan their production according to their allowances on hand. A Stackelberg game model is formulated to analyze the decisions of the government and firms aiming to maximize the social welfare of the region and maximize the profit of each firm. In view of the non-concavity and discreteness of the decision model for the government, we propose a hybrid algorithm to solve the game model efficiently. This algorithm consists of a polynomial time dynamic programming, binary search, and genetic algorithm. Results reveal that i) the Stackelberg game model greatly supports local governments' policy-making on the market-driven emission allowance trading scheme, and that ii) the social welfare is a great metric for policy-making decisions on environmental regulations. The market-driven emission trading scheme is an effective mechanism for local governments to induce emission reduction through green technology adoption by firms. However, governments should set their emission reduction targets appropriately because a tight or easy regulation policy significantly affects the environmental and economic benefits as well as the social welfare.

关键词： Emission trading scheme Local government Social welfare Game theory hybrid algorithm

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An innovative hybrid algorithm to solve nonconvex economic load dispatch problem with or without valve point effects

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INTERNATIONAL TRANSACTIONS ON ELECTRICAL ENERGY SYSTEMS 2021年第1期31卷 p1-67页

作者： Parouha, Raghav Prasad Verma, Pooja Indira Gandhi Natl Tribal Univ Dept Math Amarkantak Madhya Pradesh India

This paper presents an innovative hybrid algorithm (ihPSODE) for economic load dispatch (ELD) problems with or without valve-point effects. It integrated with suggested novel PSO (nPSO) and DE (nDE). Where in nPSO, a new, inertia weight and acceleration coefficient as well as position update equation are familiarized, to escape stagnation. And in nDE, a new, mutation strategy and crossover rate is introduced, to avoid premature convergence. In order to balance between global and local search capability, after calculation of ihPSODE population, best half member has been identified and rest members discarded. Further, in current population nPSO is employed to maintain exploration and exploitation, then nDE is used to enhance convergence accuracy. The proposed ihPSODE and its suggested component nPSO and nDE have been tested over IEEE CEC2006 (it comprising linear, nonlinear, polynomial, quadratic, and cubic functions), IEEE CEC2014, and IEEE CEC2017 (these test suites consist of unimodal, multimodal, hybrid, and composition functions) well-known benchmark functions. Then, the efficacy of the proposed algorithms has been demonstrated on nonconvex economic load dispatch problems which consist of 3-, 6-, 15-, 40-, and 140-unit test systems. The comparative results with some of the state-of-the-art algorithms confirm the effectiveness of the proposed algorithms in both aspects of better quality solutions and higher efficiency.

关键词： benchmark function differential evolution economic load dispatch hybrid algorithm particle swarm optimization

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A multiblock joint PDF finite-volume hybrid algorithm for the computation of turbulent flows in complex geometries

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JOURNAL OF COMPUTATIONAL PHYSICS 2006年第1期220卷 59-87页

作者： Rembold, B. Jenny, P. ETH Inst Fluid Dynam Dept Mech Engn CH-8092 Zurich Switzerland

A new hybrid joint probability density function (JPDF) solution algorithm for turbulent flows in complex 3D geometries is presented. The main focus is to demonstrate the applicability of JPDF methods for complex flows as observed in industrial applications. All elements of the algorithm are explained in detail and extensive validation studies are presented. A multiblock finite-volume solver, capable of handling globally unstructured, locally structured grids, was implemented together with a Lagrangian particle method. Efficient and robust particle management and accurate schemes for estimation and interpolation of particle statistics have been developed. For numerical efficiency particle sub-time stepping and an implicit finite-volume solver are applied. A fast coupling strategy was developed together with a multigrid method that allows very fast convergence on refined grids. Comparison with an established JPDF code for a bluff-body stabilized flow shows very good agreement. Furthermore, robustness and consistency of the algorithm for turbulent flow simulations with complex geometry is demonstrated. (c) 2006 Elsevier Inc. All rights reserved.

关键词： PDF methods particle methods Monte Carlo hybrid algorithm turbulence modeling finite-volume complex geometries multiblock

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Multi-objective coordination optimal model for new power intelligence center based on hybrid algorithm

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Journal of Central South University 2009年第4期16卷 683-689页

作者：刘吉成牛东晓乞建勋 School of Business Administration North China Electric Power University

In order to resolve the coordination and optimization of the power network planning effectively, on the basis of introducing the concept of power intelligence center (PIC), the key factor power flow, line investment and load that impact generation sector, transmission sector and dispatching center in PIC were analyzed and a multi-objective coordination optimal model for new power intelligence center (NPIC) was established. To ensure the reliability and coordination of power grid and reduce investment cost, two aspects were optimized. The evolutionary algorithm was introduced to solve optimal power flow problem and the fitness function was improved to ensure the minimum cost of power generation. The gray particle swarm optimization (GPSO) algorithm was used to forecast load accurately, which can ensure the network with high reliability. On this basis, the multi-objective coordination optimal model which was more practical and in line with the need of the electricity market was proposed, then the coordination model was effectively solved through the improved particle swarm optimization algorithm, and the corresponding algorithm was obtained. The optimization of IEEE30 node system shows that the evolutionary algorithm can effectively solve the problem of optimal power flow. The average load forecasting of GPSO is 26.97 MW, which has an error of 0.34 MW compared with the actual load. The algorithm has higher forecasting accuracy. The multi-objective coordination optimal model for NPIC can effectively process the coordination and optimization problem of power network.

关键词： power intelligence center (PIC) coordination optimal model power network planning hybrid algorithm

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Bus driver duty optimization using an integer programming and evolutionary hybrid algorithm

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CENTRAL EUROPEAN JOURNAL OF OPERATIONS RESEARCH 2013年第4期21卷 745-755页

作者： Kecskemeti, Balazs Bilics, Adrian SimpTech Kft H-1115 Budapest Hungary

This paper describes the details of a successful application where an integer programming and evolutionary hybrid algorithm was used to solve a bus driver duty optimization problem. The task is NP-hard, therefore theoretically optimal solutions can only be calculated for very small problem instances. Our aim is to obtain solutions of good quality within reasonable time limits. We first applied an integer programming approach to a set partitioning problem. The model was solved with a column generation algorithm in a branch and bound scheme. In order to solve larger real-life problems, we have combined the integer programming method with a greedy 1+1 steady state evolutionary algorithm. The resulting hybrid algorithm was capable of providing near-optimal solutions within reasonable timescales to larger instances of the bus driver scheduling problem. We present the results and running times of our algorithm in detail, as well as possible directions of future improvements.

关键词： Duty optimization hybrid algorithm Column generation

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Quantum-inspired hybrid algorithm for integrated process planning and scheduling

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2018年第6期232卷 1105-1122页

作者： Liu, Mi Yi, Shuping Wen, Peihan Chongqing Univ Coll Mech Engn Shazheng St Chongqing 400044 Peoples R China

Process planning and job shop scheduling problems are the two classical but crucial activities in manufacturing system. With the approach of integrated process planning and scheduling, the two actual activities are combined to conduct operation selection and operation sequencing with the constraints of practical job shop status. In this article, a quantum-inspired hybrid algorithm with the objective of minimum makespan is proposed, aiming to solve integrated process planning and scheduling problems in dynamic manufacturing systems. A hybrid-coding representation is suggested, which is a three-layer structure in numerical representation and Q-bit representation adopted from quantum-inspired evolutionary algorithm. Based on the hybrid-coding representation, customized converting and repairing rules and methods are presented to generate feasible individuals. Q-gate rotation and group leader optimization algorithm are integrated systematically for the population evolution to accelerate the convergence speed of the proposed algorithm. In order to increase the diversity of population, a chaotic map called logistic map is introduced, bringing the stochastic initial individuals. Experiments show that the proposed hybrid algorithm can generate outstanding outcomes for integrated process planning and scheduling instances.

关键词： Integrated process planning and scheduling quantum-inspired evolutionary algorithm group leader optimization algorithm chaotic maps hybrid algorithm

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