FilterSSLE method based on line search technology In this article, we consider a nonlinear optimization problem with constraints. On the basis of existing research, we present an infeasible FilterSSLE based line search technique. The algorithm only solves two linear equations with the same coefficient matrix in each iteration step to obtain the iteration direction, and the equations only contain the constraint on work concentration. The scale is much smaller than that of the original one. At the same time, we adopt the Filter technology in the algorithm, which avoids the difficulty of penalty function parameter selection caused by different problems in the penalty function method, and enhances the practicability of the algorithm.

Efficient Constraint Handling based on the Adaptive Penalty Method with Balancing the Objective Function Value and the Constraint Violation Real world problems are often formularized as constrained optimization problems (COPs). Constraint handling techniques are important for efficient search, and various approaches such as penalty methods or feasibility rules have been studied. The penalty methods deal with a single fitness function by combining the objective function value and the constraint violation with a penalty factor. Moreover, the penalty factor can be flexibly adapted by feeding back information on search process in adaptive penalty methods. However, keeping the good balance between the objective function value and the constraint violation is very difficult. In this paper, we propose a new adaptive penalty method with balancing the objective function value and the constraint violation and examine its effectiveness. LSHADE is adopted as a base algorithm to evaluate search performance, and the optimization results of 28 benchmark functions provided by the CEC 2017 competition on constrained singleobjective numerical optimizations are compared with other methods. In addition, we also examine the behavioral difference between proposed method and the conventional adaptive penalty method.