Pipe routing and clamp layout for aeroengine are NP-hard computational problems and complex engineering design processes. Besides space constraints and engineering rules, there are assembly constraints between pipes and clamps, which usually lead to repeatedly modifications between pipe routing and clamp layout designs. In order to solve the problems of assembly constraints and design coupling between them, an integrated optimization method for pipe routing and clamp layout is proposed. To this end, the MOALO (multiobjective ant lion optimizer) algorithm is modified by introducing the levy flight strategy to improve the global search performance and convergence speed, and it is further used as a basic computation tool. The integrated optimization method takes pipe and clamp as a whole system and then solves the Pareto solution set of pipe-clamp layouts by using improved MOALO, where the pipe path, clamp position, and rotation angle are selected as decision variables and are further optimized. Inspired by engineering experience, a clamp-based pipe path mechanism considering regular nodes is established to deal with assembly constraint problem. The proposed method comprehensively considers engineering rules of pipe routing and clamp layout and realizes the overall layout optimization of pipe-clamp system while guaranteeing the assembly constraints between pipes and clamps. Finally, some numerical computations and routing examples are conducted to demonstrate the feasibility and effectiveness of the proposed method.
Traveling Salesman Problem (TSP) is a typical combinatorial optimization problem, and it is a NP-hard problem. The total number of routes increases exponentially with the number of cities, so it is great significance to design an effective algorithm to find the optimal solution accurately. Chicken Swarm Optimization (CSO) is a new intelligent optimization algorithm, which is mainly proposed for continuous problems. It has the advantages of fast convergence speed and high convergence accuracy. This paper proposed a Discrete Chicken Swarm Optimization (DCSO) for TSP. The CSO is discretized by introducing the methods of swap, order crossover and reverse order mutation, where the search space of the solution is enlarged, and the diversity of the solution is increased. The typical TSP models are simulated and compared with the Basic Ant Colony Optimization and Genetic Algorithm to verify the feasibility of the presented method.
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