SRIFA: Stochastic Ranking with Improved-Firefly-Algorithm for Constrained Optimization Engineering Design Problems

摘要: Firefly-Algorithm (FA) is an eminent nature-inspired swarm-based technique for solving numerous real world global optimization problems. This paper presents overview of the constraint handling techniques. It also includes a hybrid algorithm, namely Stochastic Ranking with Improved Firefly Algorithm (SRIFA) constrained real-world engineering The stochastic ranking approach broadly used to maintain balance between penalty and fitness functions. FA extensively due its faster convergence than other metaheuristic algorithms. basic modified by incorporating opposite-based learning random-scale factor improve diversity performance. Furthermore, SRIFA uses feasibility based rules objective experimented optimize 24 CEC 2006 standard functions five well-known constrained-optimization design problems from literature evaluate analyze effectiveness SRIFA. can be seen that overall computational results are better those FA. Statistical outcomes significantly superior compared evolutionary algorithms in performance, quality efficiency.