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A New Efficient Entropy Population-Merging parallel Model for Evolutionary algorithms

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2017年第1期10卷 1186-1197页

作者： Arellano-Verdejo, Javier Godoy-Calderon, Salvador Alonso-Pecina, Federico Guzman Arenas, Adolfo Antonio Cruz-Chavez, Marco IPN CIC Othon Mendizabal S-N & Av Juan Dios Batiz Mexico City 07738 DF Mexico Univ Autonoma Estado Morelos Ave Univ 1001 Cuernavaca 62209 Morelos Mexico

In this paper a coarse-grain execution model for evolutionary algorithms is proposed and used for solving numerical and combinatorial optimization problems. This model does not use migration as the solution dispersion mechanism, in its place a more efficient population-merging mechanism is used that dynamically reduces the population size as well as the total number of parallel evolving populations. Even more relevant is the fact that the proposed model incorporates an entropy measure to determine how to merge the populations such that no valuable information is lost during the evolutionary process. Extensive experimentation, using genetic algorithms over a well-known set of classical problems, shows the proposed model to be faster and more accurate than the traditional one.

关键词： Evolutionary algorithms parallel Heuristics Global Optimization parallel genetic algorithm Heuristic Spatially Structured Island genetic algorithm

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A multi-objective reliability optimization for reconfigurable systems considering components degradation

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2019年 183卷 104-115页

作者： Zhao, Jiangbin Si, Shubin Cai, Zhiqiang Northwestern Polytech Univ Sch Mech Engn Xian 710072 Shaanxi Peoples R China

Reconfigurable systems have been widely used in practical engineering, especially for the reconfigurable computing systems and reconfigurable manufacturing systems. The reliability of reconfigurable systems can be improved by components replacement or components rearrangement without changing their reliability. Combining the advantages of the rearrangement method and replacement method, an integrated method is proposed to improve the reconfigurable system reliability cost-effectively in this paper. Then, a 0-1 integer programming model of multi-objective optimization is established to obtain the reconfiguration with maximum system reliability and minimum reconfiguration cost based on the integrated method. The coarse-grained parallel genetic algorithm (CPGA) is introduced to solve the multi-objective model, while the multiple objectives problem can be converted into a single objective problem through the novel fitness function. Finally, three examples based on the production monitoring system are implemented to illustrate the effectiveness of the CPGA comparing with the replacement based genetic algorithm. The changes of optimal reconfigurations with different parameters of the fitness function and different pre-determined system reliability are also discussed based on the examples.

关键词： Reconfigurable system Multi-objective optimization Reconfiguration cost parallel genetic algorithm

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Economic Power Dispatch with Discontinuous Fuel Cost Functions using Improved parallel PSO

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2010年第1期5卷 45-53页

作者： Mahdad, Belkacem Bouktir, T. Srairi, K. El Benbouzid, M. Biskra Univ Dept Elect Engn Biskra Algeria Univ Brest Lab Brestois Mecan & Syst Brest France

This paper presents an improved parallel particle swarm optimization approach (IPPSO) based decomposed network for economic power dispatch with discontinuous fuel cost functions. The range of partial power demand corresponding to the partial output powers near the global optimal solution is determined by a flexible decomposed network strategy and then the final optimal solution is obtained by parallel Particle Swarm Optimization. The proposed approach tested on 6 generating units with smooth cost function, and to 26-bus (6 generating units) with consideration of prohibited zone effect, the simulation results compared with recent global optimization methods (Bee-OPF, GA, NITS, SA, PSO). From the different case studies, it is observed that the proposed approach provides qualitative solution with less computational time compared to various methods available in the literature survey.

关键词： parallel genetic algorithm Decomposed Network PSO Economic dispatch Optimal power flow System security Planning and control

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OPF with Environmental Constraints with Multi Shunt Dynamic Controllers using Decomposed parallel GA: Application to the Algerian Network

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2009年第1期4卷 55-65页

作者： Mahdad, B. Bouktir, T. Srairi, K. Department of Electrical Engineering University of Biskra Department of Electrical Engineering Oum Elbouaghi University

Due to the rapid increase of electricity demand, consideration of environmental constraints in optimal power flow (OPF) problems is increasingly important. In Algeria, up to 90% of electricity is produced by thermal generators (vapor, gas). In order to keep the emission of gaseous pollutants like sulfur dioxide (SO2) and Nitrogen (NO2) under the admissible ecological limits, many conventional and global optimization methods have been proposed to study the trade-off relation between fuel cost and emissions. This paper presents an efficient decomposed parallel GA to solve the multi-objective environmental/economic dispatch problem. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two subproblems are proposed: the first subproblem is related to the active power planning to minimize the total fuel cost, and the second subproblem is a reactive power planning design based in practical rules to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the algorithm proposed was tested oil the Algerian 59-bus network test and compared with conventional methods and with global optimization methods (GA, FGA, and ACO). The results show that the approach proposed can converge to the near Solution and obtain a competitive solution at a critical situation and within a reasonable time.

关键词： Environmental economic dispatch Dynamic control parallel genetic algorithm multi-objective System loadability FACTS SVC Optimal power flow System security Planning and control

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Multi-objective fuzzy multiprocessor flowshop scheduling

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APPLIED SOFT COMPUTING 2014年 21卷 139-148页

作者： Behnamian, J. Ghomi, S. M. T. Fatemi Bu Ali Sina Univ Fac Engn Dept Ind Engn Hamadan Iran Amirkabir Univ Technol Dept Ind Engn Tehran Iran

This paper considers a bi-objective hybrid flowshop scheduling problems with fuzzy tasks' operation times, due dates and sequence-dependent setup times. To solve this problem, we propose a bi-level algorithm to minimize two criteria, namely make span, and sum of the earliness and tardiness, simultaneously. In the first level, the population will be decomposed into several sub-populations in parallel and each sub-population is designed for a scalar bi-objective. In the second level, non-dominant solutions obtained from sub-population bi-objective random key genetic algorithm (SBG) in the first level will be unified as one big population. In the second level, for improving the Pareto-front obtained by SBG, based on the search in Pareto space concept, a particle swarm optimization (PSO) is proposed. We use a defuzzification function to rank the Bell-shaped fuzzy numbers. The non-dominated sets obtained from each of levels and an algorithm presented previously in literature are compared. The computational results showed that PSO performs better than others and obtained superior results. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Fuzzy hybrid flowshop scheduling Bi-objective optimization Pareto optimal solution Particle swarm optimization parallel genetic algorithm Bell-shaped fuzzy number

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Selective vehicle routing problems under uncertainty without recourse

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2014年 62卷 68-88页

作者： Allahviranloo, Mandieh Chow, Joseph Y. J. Recker, Will W. Univ Calif Irvine Inst Transportat Studies Irvine CA 92697 USA Ryerson Univ Dept Civil Engn Toronto ON M5B 2K3 Canada

We argue that the selective vehicle routing problem is more appropriate than the conventional VRP in handling uncertainty with limited resources. However, previous formulations of selective VRPs have all been deterministic. Three new formulations are proposed to account for different optimization strategies under uncertain demand (or utility) level: reliable, robust, and fuzzy selective vehicle routing problems. Three parallel genetic algorithms (PGAs) and a classic genetic algorithm are developed and compared to the deterministic solution. PGAs differ based on their communication strategies and diversity in sub-populations. Results show that a PGA, wherein communication between demes, or subpopulations, occurs in every generation and does not eliminate repeated chromosomes, outperforms other algorithms at the cost of higher computation time. A faster variation of PGA is used to solve the non-convex reliable selective VRP, robust selective VRP and the large-scale fuzzy selective VRP, consisting of 200 nodes. Large scale application demonstrates the value of fuzzy selective vehicle routing problem FSVRP in humanitarian logistics. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Selective vehicle routing problem Stochastic optimization Fuzzy optimization Reliability Robust optimization parallel genetic algorithm Humanitarian logistics

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Dynamic strategy based fast decomposed GA coordinated with FACTS devices to enhance the optimal power flow

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ENERGY CONVERSION AND MANAGEMENT 2010年第7期51卷 1370-1380页

作者： Mahdad, Belkacem Bouktir, T. Srairi, K. EL Benbouzid, M. Univ Biskra Dept Elect Engn Biskra 07000 Algeria Dept Elect Engn Oum El Bouaghi 04000 Algeria Univ Brest Lab Brestois Mecan & Syst Brest France

Under critical situation the main preoccupation of expert engineers is to assure power system security and to deliver power to the consumer within the desired index power quality. The total generation cost taken as a secondary strategy. This paper presents an efficient decomposed GA to enhance the solution of the optimal power flow (OPF) with non-smooth cost function and under severe loading conditions. At the decomposed stage the length of the original chromosome is reduced successively and adapted to the topology of the new partition. Two sub problems are proposed to coordinate the OPF problem under different loading conditions: the first sub problem related to the active power planning under different loading factor to minimize the total fuel cost, and the second sub problem is a reactive power planning designed based in practical rules to make fine corrections to the voltage deviation and reactive power violation using a specified number of shunt dynamic compensators named Static Var Compensators (SVC). To validate the robustness of the proposed approach, the proposed algorithm tested on IEEE 30-Bus, 26-Bus and IEEE 118-Bus under different loading conditions and compared with global optimization methods (GA, EGA, FGA, PSO, MTS, MDE and ACO) and with two robust simulation packages: PSAT and MAT-POWER. The results show that the proposed approach can converge to the near solution and obtain a competitive solution at critical situation and with a reasonable time. (C) 2009 Elsevier Ltd. All rights reserved.

关键词： Decomposed network parallel genetic algorithm System loadability FACTS SVC Optimal power flow System security Planning and control

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Integrating berth allocation and quay crane assignments

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2010年第6期46卷 975-990页

作者： Chang, Daofang Jiang, Zuhua Yan, Wei He, Junliang Shanghai Maritime Univ Minist Educ Container Supply Chain Technol Engn Res Ctr Shanghai 200135 Peoples R China Shanghai Jiao Tong Univ Sch Mech Engn Shanghai 200240 Peoples R China

In this study, a dynamic allocation model using objective programming for berth allocation and quay crane assignments was preliminarily developed based on rolling-horizon approach. Afterwards, a hybrid parallel genetic algorithm (HPGA), which combined parallel genetic algorithm (PGA) and heuristic algorithm, was employed to resolve the proposed model. Furthermore, a simulation was conducted to evaluate the HPGA and to execute relevant gene repair techniques. Eventually, the numerical experiments on a specific container terminal were applied to illustrate the proposed models and algorithms. In so doing, the effectiveness of the proposed approach was verified. Crown Copyright (C) 2010 Published by Elsevier Ltd. All rights reserved.

关键词： Berth allocation Quay crane assignments Objective programming Rolling-horizon technique Heuristic algorithm parallel genetic algorithm

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Optimal Power Flow with Discontinous Fuel Cost Functions Using Decomposed GA Coordinated with Shunt FACTS

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JOURNAL OF ELECTRICAL ENGINEERING & TECHNOLOGY 2009年第4期4卷 457-466页

作者： Mahdad, Belkacem Srairi, K. Bouktir, T. Benbouzid, M. El Biskra Univ Dept Elect Engn Biskra Algeria Univ Brest Lab Brestois Mecan & Syst Brest France

This paper presents efficient parallel genetic algorithm (EPGA) based decomposed network for optimal power flow with various kinds of objective functions such as those including prohibited zones, multiple fuels, and Multiple areas. Two coordinated sub problems are proposed the first sub problem is an active power dispatch (APD) based parallel GA, a global database, generated containing the best partitioned network the second subproblem is an optimal setting of control variables such as generators voltages, tap position of tap changing transformers, and the dynamic reactive power of SVC Controllers installed at a critical buses. The proposed approach tested on IEEE 6-bus, IEEE 30-bus and to 15 generating units and compared with global optimization methods (GA, DE, FGA, PSO, MDE, ICA-PSO). The results show that the proposed approach call converge to the near solution and obtain a competitive solution with a reasonable time.

关键词： parallel genetic algorithm Decomposed Network System loadability FACTS SVC Optimal power flow Planning and control

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Optimal reliability design of a system with k-out-of-n subsystems considering redundancy strategies

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2017年 167卷 572-582页

作者： Kim, Heungseob Republ Korea Air Force Acad Dept Syst Engn 635 Danjae Ro Cheongju South Korea

This study presents new reliability models for k-out-of-n systems using a structured continuous-time Markov chain. The approach makes it comfortable to identify the characteristics of the lifetime of the system, such as reliability and expected lifetime. To demonstrate the advantage, the analysis results of them for the multi-spectral camera system, which is the only payload of Korea multi-purpose satellite-2 as an example of a real system. Furthermore, existing studies on a k-out-of-n system with standby redundancy has provided the approximated reliability for it. In this paper, it is confirmed that the approximation error could have an effect on the reliability design for a system. Moreover, new versions of reliability optimization problems, redundancy allocation problem (RAP) and reliability-redundancy allocation problem(RRAP), are proposed. In order to maximize system reliability, they further determine the redundancy strategy, either active or standby redundancy for each k-out-of-n subsystem from the traditional problems. A parallel genetic algorithm is proposed for an RRAP modeled by nonlinear mixed integer programming. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Optimal reliability design k-out-of-n system Redundancy strategy Structured Markov chain Matrix-analytic method parallel genetic algorithm

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