Article:Cheng, T., Chen, M., Fleming, P.J. orcid.org/0000-0001-9837-8404 et al. (2 more authors) (2017) A novel hybrid teaching learning based multi-objective particle swarm optimization. Neurocomputing, Abstract:How to obtain a good convergence and well-spread optimal Pareto front is still a major challenge for most meta-heuristic multi-objective optimization (MOO) methods. In this paper, a novel hybrid teaching learning based particle swarm optimization (HTL-PSO) with circular crowded sorting (CCS), named HTL-MOPSO, is proposed for solving MOO problems. Specifically, the new HTL-MOPSO combines the canonical PSO search with a teaching-learning-based optimization (TLBO) algorithm in order to improve search ability and speed up search procedure. Also, CCS technique is developed to improve the diversity and spread of solutions when truncating the external elitism archive. The performance of HTL-MOPSO algorithm was tested on several well-known benchmarks problems and compared with other state-of-the-art MOO algorithms in respect of convergence and spread of final solutions to the true Pareto front. Also, the individual contributions made by the strategies of HTL-PSO and CCS are analyzed. Experimental results validate the effectiveness of HTL-MOPSO and demonstrate its superior ability to find solutions of better spread and diversity, while assuring a good convergence.

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Yangtze River is one of the world's most important cargo-carrying rivers. However, the traffic capacity is becoming the bottleneck for further developments. This has been highlighted in recent Yangtze River economic zone proposal in which the improvement of the Yangtze River traffic capacity is a key project. Efficient traffic management based on ships' trajectory length prediction is a key way to improve the traffic capacity. Yet, in existing intelligent traffic signalling systems (ITSSs), ships are supposed to travel exactly along the central line of the Yangtze River which is often not a valid assumption and has caused a number of problems. Over the past few years, traffic data have been accumulated exponentially, leading to the big data era. This trend allows more accurate prediction of ships' travel trajectory length based on historical data. In this paper, ships' historical trajectories are first grouped by using the Fuzzy C-Means clustering algorithm. The relationship between some known factors (i.e. ship speed, loading capacity, self-weight, maximum power, ship length, ship width, ship type and water level) and the resultant memberships are then modeled using Artificial Neural Networks (ANN). The trajectory length is then estimated by the sum of the predicted probabilities multiplied by the trajectory cluster centers' length. The experimental results show that the proposed method can reduce the probability of generating wrong traffic control signals by 89% over existing ITSSs. This will significantly improve the efficiency of the Yangtze river traffic management system, and increase the traffic capacity by reducing the travelling time. Index Terms-trajectory prediction, data driven, fuzzy cmeans (FCM), artificial neural networks (ANN), intelligent traffic signalling system (ITSS).

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In association with the development of intermittent renewable energy generation (REG), dynamic multiobjective dispatch faces more challenges for power system operation due to significant REG uncertainty. To tackle the problems, a day-ahead, optimal dispatch problem incorporating energy storage (ES) is formulated and solved based on a robust multiobjective optimization method. In the proposed model, dynamic multistage ES and generator dispatch patterns are optimized to reduce the cost and emissions. Specifically, strong constraints of the charging/discharging behaviors of the ES in the space-time domain are considered to prolong its lifetime. Additionally, an adaptive robust model based on minimax multiobjective optimization is formulated to find optimal dispatch solutions adapted to uncertain REG changes. Moreover, an effective optimization algorithm, namely, the hybrid multiobjective Particle Swarm Optimization and Teaching Learning Based Optimization (PSO-TLBO), is employed to seek an optimal Pareto front of the proposed dispatch model. This approach has been tested on power system integrated with wind power and ES. Numerical results reveal that the robust multiobjective dispatch model successfully meets the demands of obtaining solutions when wind power uncertainty is considered. Meanwhile, the comparison results demonstrate the competitive performance of the PSO-TLBO method in solving the proposed dispatch problems.