Research on Optimization of Freight Train ATO Based on Elite Competition Multi-Objective Particle Swarm Optimization

In view of the complex problems that freight train ATO (automatic train operation) needs to comprehensively consider punctuality, energy saving and safety, a dynamics model of the freight train operation process is established based on the safety and the freight train dynamics model in the process of its operation. The algorithm of combining elite competition strategy with multi-objective particle swarm optimization technology is introduced, and the winning particles are obtained through the competition between two elite particles to guide the update of other particles, so as to balance the convergence and distribution of multi-objective particle swarm optimization. The performance comparison experimental results verify the superiority of the proposed algorithm. The simulation experiments of the actual line verify the feasibility of the model and the effectiveness of the proposed algorithm.

Self-determinate evaluation method based on condition of weights non-dictatorial rate

The condition of weightes non-dictatorship is extended and a comprehensive evaluae method emboding self-determinate which is combined with competitive view optimization principles is built. The basic process includes simulating the model of economic man＇s self-benefit bahaviors, taking the place of experts to evaluate, bringing in the model of minimizing the sum of included angles to integrate the information of multiple objects and put the objects in order finally. The method has the advangtages of less dependendence on the subjective information, plenty of information, fair process and simple caculating. Finally, an application example is given to illustrate the effectiveness of the proposed method.

10th China Trajectory Optimization Competition:Problem description and summary of the results

From March 20,2019 to April 30,2019,the 10th China Trajectory Optimization Competition(CTOC10)was jointly held by the Chinese Society of Theoretical and Applied Mechanics and Nanjing University of Aeronautics and Astronautics.The CTOC10 focused on trajectory optimization for Jovian exploration.The team from Harbin Institute of Technology won the first prize.In this paper,first,the history of the CTOC is presented.Subsequently,the mission of the CTOC10 is introduced,and an account of the final rankings of the competition is given.Finally,trajectory optimization methods are discussed,and suggestions for practical missions are provided.

OPTIMIZING PRODUCT MIX AND BOOSTING COMPETITIVE POWER

Since the decision of the State Council in 1985 on expanding the export of electromechanical products, China’s exports of electrome-chanical products has freed itself from long fluctuation and realized fast growth. According to statistics from the Customs Office, China’s exports of electro-mechanical products in 1995 reached US$43.86 billion, increasing 25 times in 10 years, and becoming China’s first pillar products for export. While achieving fast growth in exports, product mix has also seen sig-

Hawk and pigeon's intelligence for UAV swarm dynamic combat game via competitive learning pigeon-inspired optimization

For unmanned aerial vehicle(UAV)swarm dynamic combat,swarm antagonistic motion control and attack target allocation are extremely challenging sub-tasks.In this paper,the competitive learning pigeon-inspired optimization(CLPIO)algorithm is proposed to handle the cooperative dynamic combat problem,which integrates the distributed swarm antagonistic motion and centralized attack target allocation.Moreover,the threshold trigger strategy is presented to switch two sub-tasks.To seek a feasible and optimal combat scheme,a dynamic game approach combined with hawk grouping mechanism and situation assessment between sub-groups is designed to guide the solution of the optimal attack scheme,and the model of swarm antagonistic motion imitating pigeon’s intelligence is proposed to form a confrontation situation.The analysis of the CLPIO algorithm shows its convergence in theory and the comparison with the other four metaheuristic algorithms shows its superiority in solving the mixed Nash equilibrium problem.Finally,numerical simulation verifis that the proposed methods can provide an effective combat scheme in the set scenario.

Optimal reactive power dispatch with wind power integrated using group search optimizer with intraspecific competition and le´vy walk

This paper presents the mean–variance(MV)model to solve power system reactive power dispatch problems with wind power integrated.The MV model considers the profit and risk simultaneously under the uncertain wind power(speed)environment.To describe this uncertain environment,the Latin hypercube sampling with Cholesky decomposition simulation method is used to sample uncertain wind speeds.An improved optimization algorithm,group search optimizer with intraspecific competition and le´vy walk,is then used to optimize the MV model by introducing the risk tolerance parameter.The simulation is conducted based on the IEEE 30-bus power system,and the results demonstrate the effectiveness and validity of the proposed model and the optimization algorithm.

Jupiter system exploration trajectory design: Summary of the winning solution at CTOC10

This paper presents the methods and results submitted by the winning team from Harbin Institute of Technology of the 10th China Trajectory Optimization Competition(CTOC10).The problem posed by CTOC10 requires exploring the Jupiter system using a combined spacecraft.The exploration mission consists of the detection of Jupiter’s magnetic field and an exploration of the Galilean moons.The mission is completed through three steps:problem analysis,orbital design process,and data processing.The orbital design process is mainly divided into four parts,namely,repeating groundtrack orbit design,gravity-assisted orbit design,initial orbit parameter selection,and local optimization adjustment.The designed orbit is then evaluated using a heuristic optimization algorithm applied during the data processing.Finally,six full-coverage observations of Jupiter’s magnetic field are realized under the constraints of fuel and time.The final index of the submitted result is 357.8067.

Efficient design techniques for cislunar-space VLBI spacecraft formation in GTOC8

The 8th edition of the Global Trajectory Optimization Competition(GTOC8)presented a novel concept of a space-based very-long-baseline interferometry(VLBI)telescope in cislunar space for observing selected radio sources in cosmos.It requires designing a three-spacecraft triangular formation with changeable sizes and orientations such that observation can be scheduled as efficiently as possible.We first review the problem,and then describe the methods employed by representative teams participating in the competition.Subsequently,we present the design techniques employed by the team from the Chinese Academy of Sciences,which are primarily based on orbital-geometry analysis.Two efficient trajectory patterns are summarized:million-kilometer triangular formations with symmetric circular orbits,and consecutive-lunar-flyby trajectories with Moon-to-Moon transfer orbits.These two trajectory patterns enable establishing and reconfiguring the triangular formation with sufficiently different sizes so that a number of radio sources can be observed,thus maximizing the performance index.Finally,we present a solution with the best currently known score of J=158 million km.

OCSO-CA:opposition based competitive swarm optimizer in energy efficient IoT clustering

With the advent of modern technologies,IoT has become an alluring field of research.Since IoT connects everything to the network and transmits big data frequently,it can face issues regarding a large amount of energy loss.In this respect,this paper mainly focuses on reducing the energy loss problem and designing an energy-efficient data transfer scenario between IoT devices and clouds.Consequently,a layered architectural framework for IoT-cloud transmission has been proposed that endorses the improvement in energy efficiency,network lifetime and latency.Furthermore,an Opposition based Competitive Swarm Optimizer oriented clustering approach named OCSO-CA has been proposed to get the optimal set of clusters in the IoT device network.The proposed strategy will help in managing intra-cluster and inter-cluster data communications in an energy-efficient way.Also,a comparative analysis of the proposed approach with the state-of-the-art optimization algorithms for clustering has been performed.