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This paper studied a flight gate assignment problem where both the passenger service quality and the flight taxiing distance are considered in the objective function. A more efficient flight gate assignment model is built. Based on this model, we further propose an improved particle swarm optimization algorithm to solve it. A novel "flight-to-gate" coding scheme is designed to convert a particle position vector into the priority sequence of gates for each flight. Some tournament selection comparison rules are employed to update the position vectors of particles where a small part of infeasible individuals are kept in the iterative process of PSO. In order to improve the performance of PSO algorithm, a local search strategy based on swap operation is introduced to exploit more latitude of search space to anchor the global optimum. The algorithm is tested based on the simulated operational data from the actual airport. Experimental results reveal that our algorithm can achieve a good performance. more...

The pension robot can guide the elderly in community. The path planning environment of community for pension robot is so complicated that it is a NP-Hard problem in the optimization research. The local optimization solution is solved by the traditional swarm intelligent algorithm in path optimization problem. The novel model of path planning with community pension robot is presented, and the Quantum Particle Swarm Optimization algorithm is proposed for path planning. The robot can walk with global path in community, so as to avoid obstacle collision. The simulation results are implemented for verifying effective and robust algorithm, as well as the algorithm can overcome the shortcoming by traditional swarm intelligent algorithm. The performance is obviously improved for robot path planning. more...

This paper studied a flowshop scheduling problem with fuzzy due dates where the objective of maximizing the sum of customer satisfaction for the completion times of jobs is concerned. A self-adaptive differential evolution (SDE) algorithm has been employed to solve the fuzzy flowshop problem (FFP). The mutation scaling factor F and crossover probability CR are self-adaptive during the evolutionary process which can balance the exploration and the exploitation effectively. In order to make the SDE algorithm more effective and efficient, a local search strategy based on insert operation is introduced to exploit more latitude of search space to anchor the global optimum. The experimental results on the problem instance from literature demonstrate the good performance of the proposed SDE, thereby proving its worth as an attractive alternative for flowshop problems in uncertain environments. more...

This paper introduces an improved differential evolution algorithm for solving short-term multipurpose multistage batch scheduling problems (MMBSP) with the objective of minimizing the maximum completion time of all batches. A novel individual encoding and decoding scheme is designed for representing a scheduling solution for MMBSP. To improve the performance of DE further, a local search mechanism is introduced to exploit more latitude of search space to anchor the global optimum. In addition, the proposed DE is combined with a position bound disturbance strategy to add population diversity. Using those strategies, the proposed DE algorithm can get good balance between exploitation and exploration. The experimental results on some random generated problem instances demonstrate the good performance of the proposed DE. more...

An improved cloud resource revenue optimization models are proposed, which include the benefits of service providers and users negotiate SLA agreement. And the models define the constraint mechanism for describing service quality problems such as service response time and customer satisfaction. We design the comprehensive response indicator and use this indicator to quantify the rationality of the distribution of resources, which are the equilibrium between maximal revenue of cloud service providers and the quality of service for users. The parallel PSO-ACO algorithm based on MapReduce for distributed systems is presented to solve the above model, which represents the massive resource allocation optimization problem. The experiments are implemented to analyze the algorithm performance and task allocation strategy. And the experiments results show that the models and algorithms are robust and efficient. more...