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Operation sequencing is one of the crucial tasks in process planning. However, it is an intractable process to identify an optimized operation sequence with minimal machining cost in a vast search space constrained by manufacturing conditions. In this paper, the complicated operation sequencing process has been modelled as a combinatorial optimization problem, and a modern evolutionary algorithm, i.e. the particle swarm optimization (PSO) algorithm, has been employed and modified to solve it effectively. Initial process plan solutions are formed and encoded into particles of the PSO algorithm. The particles ‘fly’ intelligently in the search space to achieve the best sequence according to the optimization strategies of the PSO algorithm. Meanwhile, to explore the search space comprehensively and to avoid being trapped into local optima, several new operators have been developed to improve the particles' movements, forming a modified PSO algorithm. A case study involving three prismatic parts has been used to verify the performance and efficiency of the modified PSO algorithm. A comparison has been made between the result of the modified PSO algorithm and the previous results using the genetic algorithm (GA) and the simulated annealing (SA) algorithm and the different characteristics of the three algorithms are indicated. Case studies show that the developed PSO can generate satisfactory results in optimizing the process planning problem.

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A true quantum signature algorithm based on continuous-variable entanglement state is proposed. In the suggested algorithm, a key-pair, i.e. private signature key and public verification key, is generated based on a one-way function. By employing the signature key, a message state is encoded into a 2k-particle entangled state and a two-particle entangled state is prepared. The resulting states are exploited as a signature of the message state. The signature can be decoded under the verification key when it needs to be verified. Subsequently, a decoded message state and a two-particle entangled state are obtained. To compare the decoded states and the original states, a quantum circuit for comparing these states is exploited. Making use of measurement results of the quantum circuit one can judge the authenticity of the received signature. According to the security requirement of the signature scheme, the suggested algorithm has been proven to be theoretically secure by using the Shannon information theory.

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For SLA-aware service composition problem (SSC), an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO) is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is designed by introducing crossover operator. In order to restrain particle swarm’s premature convergence and increase its global search capacity, the swarm diversity indicator is introduced and a particle mutation strategy is proposed to increase the swarm diversity. To accelerate the process of obtaining the feasible particle position, a local search strategy based on constraint domination is proposed and incorporated into the proposed algorithm. At last, some parameters in the algorithm HMDPSO are analyzed and set with relative proper values, and then the algorithm HMDPSO and the algorithm HMDPSO+ incorporated by local search strategy are compared with the recently proposed related algorithms on different scale cases. The results show that algorithm HMDPSO+ can solve the SSC problem more effectively.

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