Random orthocenter strategy in interior search algorithm and its engineering application

Abstract

Determining how to improve the global search ability and adaptability of an algorithm without reducing the convergence speed is still a major challenge for most meta-heuristic algorithms. This paper proposes a new random orthocenter strategy combined with a Levy flight strategy to improve the interior search algorithm (ISA). The random orthocenter strategy is to randomly select a point outside the element and mirror to form a triangle and to solve the image of the element based on the orthocentre, which offsets the unique control parameters in the algorithm. The Levy flight strategy further prevents the algorithm from falling into local optimization. Thirteen benchmark functions and two engineering problems are selected for simulation tests. The experimental results show that the random orthocenter ISA significantly improves the global optimization and adaptability and has advantages on application in complex practical engineering optimization problems.