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Tool-path airtime optimization problem during multi-contour processing in leather cutting is regarded as generalized traveling salesman problem. A hybrid intelligence algorithm is proposed. The improved genetic simulated annealing algorithm is applied to optimize cutting path selected arbitrarily firstly, and an optimal contour sequence is founded, then problem is changed into multi- segment map problem solved with dynamic programming algorithm. The algorithm's process and its various parameters are given simultaneously, and its performance is compared with simulated annealing and standard genetic algorithm alone. The results show that the algorithm is more effective.

Balancing convergence and diversity has become a key point especially in many-objective optimization where the large numbers of objectives pose many challenges to the evolutionary algorithms. In this paper, an opposition-based evolutionary algorithm with the adaptive clustering mechanism is proposed for solving the complex optimization problem. In particular, opposition-based learning is integrated in the proposed algorithm to initialize the solution, and the nondominated sorting scheme with a new adaptive clustering mechanism is adopted in the environmental selection phase to ensure both convergence and diversity. The proposed method is compared with other nine evolutionary algorithms on a number of test problems with up to fifteen objectives, which verify the best performance of the proposed algorithm. Also, the algorithm is applied to a variety of multiobjective engineering optimization problems. The experimental results have shown the competitiveness and effectiveness of our proposed algorithm in solving challenging real-world problems.

Double-workshop mixed chlor-alkali planned model production process is a multi-level multi-product multi-constraint mixed batch planning problem production line model, taking into account the actual production of joint production of dual-workshop mode of production to determine the products and materials at various workshops, various Plan period on the production quantity, at the end of each period to meet the delivery needs of the type and quantity simultaneously, during the product implementation plan the goal of profit maximization. It uses particle swarm algorithm to reach the answer, indicating the feasibility of the method and effectiveness through examples of the calculation.