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Optimum design of rolling element bearings using a genetic algorithm-differential evolution (GA-DE) hybrid algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2011年第C3期225卷 714-721页

作者： Lin, W. Y. De Lin Inst Technol Dept Mech Engn Tucheng City 236 Peoples R China

Binary-code genetic algorithms (BGA) have been used to obtain the optimum design for deep groove ball bearings, based on maximum fatigue life as an objective function. The problem has ten design variables and 20 constraint conditions. This method can find better basic dynamic loads rating than those listed in standard catalogues. However, the BGA algorithm requires a tremendous number of evaluations of the objective function per case to achieve convergence (e.g. about 5 200 000 for a representative case). To overcome this difficulty, a hybrid evolutionary algorithm by combining real-valued genetic algorithm (GA) with differential evolution (DE) is used together with the proper handling of constraints for this optimum design task. Findings show that the GA-DE algorithm can successfully find the better dynamic loads rating, about 1.3-11.1per cent higher than those obtained using the traditional BGA. Moreover, the mean number of evaluations of the objective function required to achieve convergence is about 3011, using the GA-DE algorithm, as opposed to about 5 200 000 for a representative case using the BGA. Comparison shows the GA-DE algorithm to be much more effective and efficient than the BGA.

关键词： rolling element bearing genetic algorithm differential evolution hybrid evolutionary algorithm optimum design

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Solving a multi-objective open shop scheduling problem by a novel hybrid ant colony optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2011年第3期38卷 2817-2822页

作者： Panahi, Nadi Tavakkoli-Moghaddam, Reza Univ Tehran Dept Ind Engn Coll Engn POB 11155-4563 Tehran Iran

This paper considers an open shop scheduling problem that minimizes bi-objectives. namely makespan and total tardiness. This problem, due to its complexity, is ranked in the class of NP-hard problems. In this case, traditional approaches cannot reach to an optimal solution in a reasonable time. Thus, we propose an efficient method based on multi-objective simulated annealing and ant colony optimization, in order to solve the given problem. Furthermore a decoding operator is applied in order to improve the quality of generated schedules. Finally, we compare our computational results with a well-known multi-objective genetic algorithm, namely NSGA II. In addition, comparisons are made in single objective case. The outputs show encouraging results in the form of the solution quality. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Scheduling Open shop Multi-objective optimization hybrid evolutionary algorithm

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A multi-objective framework for multi-area economic emission dispatch

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ENERGY 2018年 154卷 126-142页

作者： Narimani, Hossein Razavi, Seyed-Ehsan Azizivahed, Ali Naderi, Ehsan Fathi, Mehdi Ataei, Mohammad H. Narimani, Mohammad Rasoul Islamic Azad Univ Kermanshah Branch Dept Elect Engn Kermanshah Iran Univ Birjand Dept Elect Engn Birjand Iran Univ Technol Sydney Dept Elect Engn Sydney NSW Australia Razi Univ Fac Engn & Technol Eslam Abad Gharb Kermanshah Iran Missouri Univ Sci & Technol Elect & Comp Engn Rolla MO 65409 USA

Economic dispatch seeks for the most economical combination of generation units in power system while satisfying bunch of physical and operational constraints. Finding the most economical generation strategy in power markets with several generation zones is crucial for power system operation. Conventional economic dispatch models cannot find the most economical generation schedule in power system with different generation zones. Moreover, the most economical schedule of generation cannot satisfy the environmental expectations. Therefore, compromising between generation cost and environmental issues is unavoidable. In this connection, total emission produces by generation units is taken into consideration as an objective function in concert with generation cost function. Furthermore, different operational constraints including valve-point effect, prohibited operating zones and multi-fuel operation are considered to make the proposed approach more realistic. Considering all these restrictions necessitate solving the proposed problem, i.e. multi-objective multi-area economic dispatch problem, by a reliable and strong optimization algorithm. In this regard, a hybrid evolutionary algorithm based on the shuffle frog leaping algorithm and the particle swarm optimization is proposed to solve the proposed problem. Effectiveness of the proposed hybrid algorithm is verified on different test systems. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Multi-objective multi-area economic dispatch hybrid evolutionary algorithm Emission Valve-point effect Prohibited operating zone Multi-fuel operation

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Analysis the securable operation and protection coordination indices on multi-objective sitting and sizing of synchronous distributed generations on distribution networks

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IET GENERATION TRANSMISSION & DISTRIBUTION 2023年第9期17卷 2169-2181页

作者： Shad, Hossien Gandomkar, Majid Nikoukar, Javad Islamic Azad Univ Coll Elect Engn & Comp Dept Elect Engn Saveh Branch POB 39197-15179 Saveh Iran

Despite improving various technical indices such as operation and voltage profile, the integration of synchronous distributed generations (SDGs) may lead to the loss of protection coordination in the distribution network (DN). To tackle the problem, this paper presents a multi-objective siting and sizing of SDGs on DN considering operation, economic and protection coordination indices simultaneously. The proposed scheme is structured in the framework of a four-objective optimization problem, which aims to minimize the energy losses, the worst voltage security index (VSI), planning cost of SDGs and the protection index (PI) (the deviation of coordination time interval). Therefore, it is constrained to power flow equations, VSI, and protection coordination of overcurrent relays (OCRs). Then, the Pareto optimization based on the method of the weighted functions summation obtains an integrated single-objective problem for the proposed scheme. Next, a hybrid evolutionary algorithm formed by merging particle swarm optimization (PSO) and crow search algorithm (CSA) is incorporated to achieve the optimal solution with unique response approximate conditions. Eventually, the suggested scheme is applied on distribution portion of IEEE 30-bus ring and IEEE 69-bus radial DN and numerical results confirm the efficient performance of SDG planning in terms of technical indicators and protective devices coordination.

关键词： distribution network hybrid evolutionary algorithm planning protection coordination securable operation sitting and sizing synchronous distributed generation

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A Fast and Robust Heuristic algorithm for the Minimum Weight Vertex Cover Problem

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IEEE ACCESS 2021年 9卷 31932-31945页

作者： Wang, Yang Lu, Zhipeng Punnen, Abraham P. Huazhong Univ Sci & Technol Sch Comp Sci & Technol SMART Wuhan 430074 Peoples R China Simon Fraser Univ Surrey Dept Math Surrey BC V3T 0A3 Canada

The minimum weight vertex cover problem (MWVCP) is a fundamental combinatorial optimization problem with various real-world applications. The MWVCP seeks a vertex cover of an undirected graph such that the sum of the weights of the selected vertices is as small as possible. In this paper, we present an effective algorithm to solve the MWVCP. First, a master-apprentice evolutionary algorithm based on two individuals is conducted to enhance the diversity of solutions. Second, a hybrid tabu search combined configuration checking and solution-based tabu search is introduced to intensify local search procedure. Harnessing the power of the evolutionary strategy and a novel variant of hybrid tabu search, Master-Apprentice evolutionary algorithm with hybrid Tabu Search, MAE-HTS, is presented. Results of extensive computational experiments using standard benchmark instances and other large-scale instances demonstrate the efficacy of our algorithm in terms of solution quality, running time, and robustness compared to state-of-the-art heuristics from the literature and the commercial MIP solver Gurobi. We also systematically analyze the role of each individual component of the algorithm which when worked in unison produced superior outcomes. In particular, MAE-HTS produced improved solutions for 2 out of 126 public benchmark instances with better running time. In addition, our MAE-HTS outperforms other state-of-the-art algorithms DLSWCC and NuMWVC for 72 large scale MWVCP instances by obtaining the best results for 64 ones, while other two reference algorithms can only obtain 27 best results at most.

关键词： Search problems evolutionary computation Benchmark testing Optimization hybrid power systems Heuristic algorithms Systematics hybrid evolutionary algorithm metaheuristics minimum weight vertex cover problem solution-based tabu search

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Robust multi-objective vehicle routing problem with time windows for hazardous materials transportation

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IET INTELLIGENT TRANSPORT SYSTEMS 2020年第3期14卷 154-163页

作者： Men, Jinkun Jiang, Peng Xu, Huan Zheng, Song Kong, Yaguang Hou, Pingzhi Wu, Feng Hangzhou Dianzi Univ Sch Automat Baiyang St Hangzhou 310000 Peoples R China

This work focuses on a hazardous material (HazMat) vehicle routing problem with time windows (VRPTW). Given the multi-objective and uncertainty natures of HazMat transportation, a multi-objective robust VRPTW (MO-RVRPTW) model is proposed, which simultaneously optimise both the number of vehicles and the uncertain transportation risk. An uncertain set containing 32 potential incident scenarios is constructed to model the uncertain parameter. To handle the uncertain multi-objective problem (MOP), this work develops two versions of robust criterion to transform the MO-RVRPTW to its robust counterpart. A hybrid evolutionary algorithm (HEA) is designed to solve the robust counterpart, which integrates a push forward insertion heuristic for initial population construction, a route exchange crossover operator and a multi-component mutation (MCM) operator for generating the better offspring. The MCM is based on three basic local search operators and employs a sequential-move mechanism to improve the effectiveness of the algorithm. The proposed algorithm is tested on classical Solomon instances. Experiment results show that HEA is competitive in terms of convergence and diversity. A deterministic case is employed to justify the proposed robust criterion. In most cases, they can provide a set of robust non-dominated solutions with respect to both pareto optimality and robust optimality.

关键词： vehicle routing search problems Pareto optimisation transportation optimisation hazardous materials genetic algorithms vehicles evolutionary computation MO-RVRPTW uncertain transportation risk uncertain set 32 potential incident scenarios uncertain parameter uncertain multiobjective problem robust criterion robust counterpart hybrid evolutionary algorithm initial population construction route-exchange crossover operator multicomponent mutation operator basic local search operators robust optimality robust multiobjective vehicle routing problem time windows hazardous materials transportation hazardous material vehicle routing problem uncertainty natures HazMat transportation problem multiobjective robust VRPTW model

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An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2011年第2期24卷 306-317页

作者： Niknam, Taher Fard, Elahe Taherian Pourjafarian, Narges Rousta, Alireza Shiraz Univ Technol Elect & Elect Dept Shiraz Iran Shiraz Univ Shiraz Iran

Clustering techniques have received attention in many fields of study such as engineering, medicine, biology and data mining. The aim of clustering is to collect data points. The K-means algorithm is one of the most common techniques used for clustering. However, the results of K-means depend on the initial state and converge to local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Modify Imperialist Competitive algorithm (MICA) and K-means (K), which is called K-MICA, for optimum clustering N objects into K clusters. The new hybrid K-ICA algorithm is tested on several data sets and its performance is compared with those of MICA, ACO, PSO, Simulated Annealing (SA), Genetic algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handling data clustering. (C) 2010 Elsevier Ltd. All rights reserved.

关键词： Imperialist competitive algorithm (ICA) Data clustering K-means clustering hybrid evolutionary algorithm

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A new hybrid bacterial foraging and simplified swarm optimization algorithm for practical optimal dynamic load dispatch

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期49卷 414-429页

作者： Azizipanah-Abarghooee, Rasoul Islamic Azad Univ Dept Elect Engn Marvdasht Branch Marvdasht Iran

This paper presents a novel approach to depict the practical constraints of generator units such as reserve constraints, prohibited operating zones and valve-point effects in the optimal dynamic dispatch problem. Determining the power generation output of units at minimum total fuel cost a dynamic environment with ramp rate limits to satisfy load demand and transmission losses is too complicated and has a lot of local optima in its search space. In addition, the proposed problem has a non-linear, non-convex, non-smooth, multi-modal, non-separable, and non-differentiable nature. In order to overcome above problems a new hybrid technique, based on bacterial foraging and simplified swarm optimization algorithms combined with a new mutation operator and opposition-based initialization is proposed to restraint the premature convergence of the solutions. Therefore, the bacteria in chemo-tactic procedure are moved in short or long steps as well as swimming movements. Furthermore, to increase the diversity of the solution of the search space a novel self-adaptive mutation strategy which profits from four mutation rules is implemented. For more validation the simulation results are applied on four small, medium and large scale systems with 5-unit, 10-unit, 30-unit and 100-unit and compared with those of other methods in the area. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Bacterial foraging hybrid evolutionary algorithm Optimal dynamic dispatch Practical constraints Self adaptive mutation strategy Simplified swarm optimization

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Performance assessment of PSO, DE and hybrid PSO-DE algorithms when applied to the dispatch of generation and demand

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2013年第1期47卷 205-217页

作者： Araujo, Thais de Fatima Uturbey, Wadaed Fed Univ Minas Gerais UFMG Grad Program Elect Engn BR-31270010 Belo Horizonte MG Brazil Fed Univ Minas Gerais UFMG Dept Elect Engn BR-31270010 Belo Horizonte MG Brazil

This work presents a comparison of three evolutionary algorithms, the particle swarm optimization, the differential evolution algorithm and a hybrid algorithm derived from the previous, when applied to the generation and demand dispatch problem. An optimization problem is formulated in the context of a small grid with partially flexible demand that can be shifted along a time horizon. It is assumed that grid operator dispatches generation and flexible demand along the time horizon aiming at minimizing generation costs. Consumption restrictions associated with flexible demand are modeled by equality and inequality energy constraints. Power flow equality constraints and inequality constraints due to operational limits for each dispatch interval are represented. The paper discusses a methodology for evolutionary algorithms performance assessment and states the importance of using statistical tools. The comparison is initially conducted using the IEEE 30-bus test system. Problem dimension effect is addressed considering different number of dispatch intervals in the time horizon. Moreover, the algorithms are applied to the 192-bus system of a Brazilian distribution utility, in the particular context of a load management program for large consumers of the company. In this application, the quality of the near-optimal solution obtained with the stochastic algorithms is evaluated by comparing with an analytical optimization algorithm solution. (c) 2012 Elsevier Ltd. All rights reserved.

关键词： evolutionary algorithms comparison Demand dispatch Particle swarm optimization Differential evolution algorithm hybrid evolutionary algorithm

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Evaluating the peptide structure prediction capabilities of a purely ab-initio method

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PROTEIN ENGINEERING DESIGN & SELECTION 2017年第10期30卷 723-727页

作者： Amitay, M. Goldstein, M. Jerusalem Coll Technol Dept Bioinformat Havaad Haleumi 21 IL-9372115 Jerusalem Israel Jerusalem Coll Technol Dept Comp Sci Havaad Haleumi 21 IL-9372115 Jerusalem Israel

DEEPSAM is a relatively new global optimization algorithm aimed to predict the structure of biomolecules from sequence, without any additional preliminary assumption. It is an evolutionary algorithm whose mutation operators are built by hybridizing the diffusion equation method, molecular dynamics simulated annealing, and a quasi- Newton local minimization method. The goal of this study was to evaluate the structure prediction capabilities of DEEPSAM by running it upon NMR structures of linear peptides (10-20 residues). The results indicate that DEEPSAM successfully predicted the conformations of these peptides, using modest computing resources.

关键词： DEEPSAM hybrid evolutionary algorithm peptide structure prediction PES global minimization

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