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A model induced max-min ant colony optimization for asymmetric traveling salesman problem

APPLIED SOFT COMPUTING 2013年第3期13卷 1365-1375页

作者： Bai, Jie Yang, Gen-Ke Chen, Yu-Wang Hu, Li-Sheng Pan, Chang-Chun Shanghai Jiao Tong Univ Minist Educ China Dept Automat Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Minist Educ China Key Lab Syst Control & Informat Proc Shanghai 200240 Peoples R China Univ Manchester MBS Decis & Cognit Sci Res Ctr Manchester M15 6PB Lancs England

A large number of hybrid metaheuristics for asymmetric traveling salesman problem (ATSP) have been proposed in the past decades which produced better solutions by exploiting the complementary characteristics of different optimization strategies. However, most of the hybridizations are criticized due to lacking of sufficient analytical basis. In this paper, a model induced max-min ant colony optimization (MIMM-ACO) is proposed to bridge the gap between hybridizations and theoretical analysis. The proposed method exploits analytical knowledge from both the ATSP model and the dynamics of ACO guiding the behavior of ants which forms the theoretical basis for the hybridization. The contribution of this paper mainly includes three supporting propositions that lead to two improvements in comparison with classical max-min ACO optimization (MM-ACO): (1) Adjusted transition probabilities are developed by replacing the static biased weighting factors with the dynamic ones which are determined by the partial solution that ant has constructed. As a byproduct, nonoptimal arcs will be indentified and excluded from further consideration based on the dual information derived from solving the associated assignment problem (AP). (2) A terminal condition is determined analytically based on the state of pheromone matrix structure rather than intuitively as in most traditional hybrid metaheuristics. Apart from the theoretical analysis, we experimentally show that the proposed algorithm exhibits more powerful searching ability than classical MM-ACO and outperforms state of art hybrid metaheuristics. (C) 2012 Elsevier B. V. All rights reserved.

关键词： hybrid evolutionary algorithm Ant colony optimization ATSP Model induced

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hybrid invasive weed/biogeography-based optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 64卷 213-231页

作者： Khademi, Gholamreza Mohammadi, Hanieh Simon, Dan Cleveland State Univ Dept Elect Engn & Comp Sci Cleveland OH 44115 USA

We propose a new variant of the ecologically-inspired optimization method known as invasive weed optimization (IWO). The proposed algorithm features three new components that are typically not present in IWO: (1) migration;(2) gradient descent;and (3) mutation. In standard IWO, each individual uses only its own features (that is, independent solution variables) to randomly distribute new seeds over the search space. In other words, there is no sharing of features among individuals. We propose the application of the migration operator from biogeography-based optimization (BBO) to include the feature-sharing capability in IWO. This modification improves the quality of the distributed seeds (that is, new candidate solutions) in the population. To further improve the local search ability of IWO, we propose the use of gradient descent. Mutation is activated under certain conditions to increase the diversity of the population, and escape local optima. We demonstrate the performance of this new hybrid IWO/BBO on a set of single-objective benchmarks, and on a real-world cyber physical system problem to optimize a user intent recognition system for transfemoral amputees. hybrid IWO/BBO is compared to standard IWO, BBO, and 10 other optimization algorithms. The Kruskal Wallis and Wilcoxon signed-rank tests are used to statistically compare the algorithms. The results for hybrid IWO/BBO present promising improvements over standard IWO. For instance, out of 25 benchmarks, hybrid IWO/BBO performs better than IWO on 18 problems with dimension 30. hybrid IWO/BBO shows competitive performance with comparison to the 10 other state-of-the-art optimization algorithms. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Invasive weed optimization Biogeography-based optimization hybrid evolutionary algorithm

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Evolving pattern recognition systems

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IEEE TRANSACTIONS ON evolutionary COMPUTATION 2002年第6期6卷 594-609页

作者： Rizki, MM Zmuda, MA Tamburino, LA Wright State Univ Dept Comp Sci & Engn Dayton OH 45435 USA Miami Univ Dept Comp Sci Oxford OH 45402 USA AFRL SNAT Wright Patterson AFB OH 45433 USA

A hybrid evolutionary learning algorithm is presented that synthesizes a complete multiclass pattern recognition system. The approach uses a multifaceted representation that evolves layers of processing to perform feature extraction from raw input data, select cooperative sets of feature detectors, and assemble a linear classifier that uses the detectors' responses to label targets. The hybrid algorithm, hybrid evolutionary Learning for Pattern Recognition (HELPR) blends elements of evolutionary programming, genetic programming, and genetic algorithms to perform a search for an effective set of feature detectors. Individual detectors are represented as expressions composed of morphological and arithmetic operations. Starting with a few small random expressions, HELPR expands the number and complexity of the features to produce a recognition system that achieves high accuracy. Results are presented that demonstrate the performance of HELPR-generated recognition systems applied to the task of classification of high-range resolution radar signals.

关键词： classification evolutionary computation feature extraction feature selection high-range resolution (HRR) analysis hybrid evolutionary algorithm mathematical morphology pattern recognition

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Performance evaluation of feed-forward neural network with soft computing techniques for hand written English alphabets

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APPLIED SOFT COMPUTING 2011年第1期11卷 1156-1182页

作者： Shrivastava, Saurabh Singh, Manu Pratap Bundelkhand Univ Dept Math Sci & Comp Applicat Jhansi Uttar Pradesh India Dr BR Ambedkar Univ Inst Comp & Informat Sci Dept Comp Sci Agra Uttar Pradesh India

This paper describes the performance evaluation for the feed forward neural network with three different soft computing techniques to recognition of hand written English alphabets. evolutionary algorithms for the hybrid neural network are showing the numerous potential in the field of pattern recognition. We have taken five trials and two networks of each of the algorithm: back propagation, evolutionary algorithm, and hybrid evolutionary algorithm respectively. These algorithms have been taken the definite lead on the conventional approaches of neural network for pattern recognition. It has been analyzed that the feed forward neural network by two evolutionary algorithms makes better generalization accuracy in character recognition problems. The problem of not convergence the weight in conventional backpropagation has also eliminated by using the soft computing techniques. It has been observed that, there are more than one converge weight matrix in character recognition for every training set. The results of the experiments show that the hybrid evolutionary algorithm can solve challenging problem most reliably and efficiently. These algorithms have also been compared on the basis of time and space complexity for the training set. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Character recognition hybrid evolutionary algorithm Soft computing Feed-forward neural networks

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An Enhanced hybrid particle swarm optimization and simulated annealing for practical economic dispatch

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ENERGY EDUCATION SCIENCE AND TECHNOLOGY PART A-ENERGY SCIENCE AND RESEARCH 2012年第1期30卷 553-564页

作者： Niknam, Taher Azizipanah-Abarghooee, Rasoul Sedaghati, Reza Kavousi-Fard, Abdollah Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Dept Elect Engn Beyza Branch Beyza Iran Islamic Azad Univ Young Researchers Club Sarvestan Branch Sarvestan Iran

This paper presents an effective evolutionary method for solving Economic Dispatch (ED). ED has been widely used in power system operation and planning for determination of electricity prices. In this study, some restrictions such as prohibited operating zones and valve point effects beside the multi fuel type of generation units should be taken into account in order to getting closer to the real condition of power systems. Furthermore, to ensure secure real-time power system operations, the system operator must schedule sufficient resources to meet energy demand. This problem is a complex optimization problem spontaneously which its complexity is increased with considering all above constraints. This paper presents Particle Swarm Optimization (PSO) to solve ED problem, furthermore in attempt to reduce processing time and improve the quality of solutions, particularly to avoid being trapped in local optima, PSO algorithm is mixed with Simulated Annealing (SA) algorithm. In addition, a new self adaptive mutation strategy is presented to expand the search ability of the proposed hybrid algorithm. The performance of the hybrid PSO-SA has been tested on three typical systems and compared with others in literatures. The comparison results show that the efficiency of proposed approach can reach higher quality solution and faster computational time than the conventional methods.

关键词： Economic dispatch hybrid evolutionary algorithm PSO SA Prohibited operating zones Multi-fuel options Valve-point effects

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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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Multi-objective reactive power market clearing in competitive electricity market using HFMOEA

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APPLIED SOFT COMPUTING 2013年第4期13卷 2087-2103页

作者： Saini, Ashish Saraswat, Amit Dayalbagh Educ Inst Fac Engn Dept Elect Engn Agra 282110 Uttar Pradesh India

This paper presents an application of a hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) for solving a highly constraint, mixed integer type, complex multi-objective reactive power market clearing (RPMC) problem for the competitive electricity market environment. In HFMOEA based multi-objective optimization approach, based on the output of a fuzzy logic controller crossover and mutation probabilities are varied dynamically. It enhances stochastic search capabilities of HFMOEA. In multi-objective RPMC optimization framework, two objective functions namely the total payment function (TPF) for reactive power support from generators and synchronous condensers and the total real transmission loss (TRTL) are minimized simultaneously for clearing the reactive power market. The proposed HFMOEA based multi-objective RPMC scheme is tested on a standard IEEE 24 bus reliability test system and its performance is compared with five other multi-objective evolutionary techniques such as MOPBIL, NSGA-II, UPS-EMOA and SPEA-2 and a new extended form of NSGA (ENSGA-II). Applying all these six evolutionary techniques, a detailed statistical analysis using T-test and boxplots is carried out on three performance metrics (spacing, spread and hypervolume) data for RPMC problem. The obtained simulation results confirm the overall superiority of HFMOEA to generate better Pareto-optimal solutions with higher convergence rate as compared to above mentioned algorithms. Further, TPF and TRTL values corresponding to the best compromise solutions are obtained using said multi-objective evolutionary techniques. These values are compared with one another to take better market clearing decisions in competitive electricity environment. (C) 2012 Elsevier B.V. All rights reserved.

关键词： Reactive power market clearing Competitive electricity market Multi-objective optimization Pareto-optimal front Fuzzy logic controller hybrid evolutionary algorithm

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A Bi-Objective Lane Reservation Problem Considering Dynamic Traffic Flow

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2023年第1期24卷 367-381页

作者： Li, Tao Wang, Nengmin Jiang, Bin Zhang, Meng Xi An Jiao Tong Univ Sch Management Xian 710049 Peoples R China ERC Proc Min Mfg Serv Shaanxi Prov Xian 710049 Peoples R China De Paul Univ Dept Management Driehaus Coll Business Chicago IL 60604 USA

Lane reservation strategies are widely used to ensure the right of way for eco-friendly vehicles and encourage people to green their commute. In most lane reservation problems (LRPs), the parameters underlying the system traffic conditions (e.g., vehicle speed and traffic flow) cannot be effectively specified. This paper addresses a new dynamic lane reservation problem (DLRP), which aims to optimize lanes that need to be reserved in different time periods based on trajectory data for existing transit network optimization. For passengers in reserved lanes, their travel time is minimized to guarantee traffic priority. Considering that the reserved lanes cause travel time growth on regular lanes, an improved multiobjective mixed integer nonlinear programming (MINLP) is established to minimize the delay. The problem complexity of this paper is NP-hard. This paper applies a preprocessing method for the actual traffic flow data analysis for link travel time calculation. We developed a hybrid evolutionary algorithm decomposing a multiobjective optimization problem (MOP) to a collection of simple MOPs. These subproblems are collaboratively solved. The hybrid crossover strategy exploits the advantages of different crossover operators for a better performance. The experimental results of MOEA/D and NSGA-II with standard test functions show that the proposed algorithm can improve the convergence and distribution of the results. Through numerous analyses and calculations of instances, the proposed algorithm is proven to be effective.

关键词： Lane reservation problem hybrid evolutionary algorithm dynamic traffic flow trajectories Pareto front

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A hybrid cuckoo search and genetic algorithm for reliability-redundancy allocation problems

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COMPUTERS & INDUSTRIAL ENGINEERING 2013年第4期66卷 1115-1124页

作者： Kanagaraj, G. Ponnambalam, S. G. Jawahar, N. Monash Univ Sch Engn Bandar Sunway 46150 Malaysia Thiagarajar Coll Engn Dept Mech Engn Madurai 625015 Tamil Nadu India

Solving reliability and redundancy allocation problems via meta-heuristic algorithms has attracted increasing attention in recent years. In this study, a recently developed meta-heuristic optimization algorithm cuckoo search (CS) is hybridized with well-known genetic algorithm (GA) called CS-GA is proposed to solve the reliability and redundancy allocation problem. By embedding the genetic operators in standard CS, the balance between the exploration and exploitation ability further improved and more search space are observed during the algorithms' performance. The computational results carried out on four classical reliability-redundancy allocation problems taken from the literature confirm the validity of the proposed algorithm. Experimental results are presented and compared with the best known solutions. The comparison results with other evolutionary optimization methods demonstrate that the proposed CS-GA algorithm proves to be extremely effective and efficient at locating optimal solutions. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Reliability and redundancy allocation Cuckoo search Genetic algorithm hybrid evolutionary algorithm

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Multi-objective optimal reactive power dispatch considering voltage stability in power systems using HFMOEA

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2013年第1期26卷 390-404页

作者： Saraswat, Amit Saini, Ashish Dayalbagh Educ Inst Dept Elect Engn Fac Engn Agra 282110 Uttar Pradesh India

This paper proposes a new hybrid fuzzy multi-objective evolutionary algorithm (HFMOEA) based approach for solving complex multi-objective, mixed integer nonlinear problems such as optimal reactive power dispatch considering voltage stability (ORPD-VS). In HFMOEA based optimization approach, the two parameters like crossover probability (Pc) and mutation probability (Pm) are varied dynamically through the output of a fuzzy logic controller. The fuzzy logic controller is designed on the basis of expert knowledge to enhance the overall stochastic search capability for generating better pareto-optimal solution. Two detailed case studies are presented: Firstly, the performance of HFMOEA is tested on five benchmark test problems such as ZDT1, ZDT2, ZDT3, ZDT4 and ZDT6 as suggested by Zitzler, Deb and Thiele;Secondly, HFMOEA is applied to multi-objective ORPD-VS problem. In both the case studies, the optimization results obtained from HFMOEA are analysed and compared with the same obtained from two versions of elitist non-dominated sorting genetic algorithms such as NSGA-II and MNSGA-II in terms of various performance metrics. The simulation results are promising and confirm the ability of HFMOEA for generating better pareto-optimal fronts with superior convergence and diversity. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Multi-objective optimization Optimal reactive power dispatch Voltage stability Pareto-optimal front Fuzzy logic controller hybrid evolutionary algorithm

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