A Hybrid Cuckoo-Gravitation Algorithm for Cost-optimized QFD Decision-Making Problem

Utilizing QFD in the process of manufacturing and service performing is confronted with an optimization problem called QFD decision-making problem (QFDDMP). Facing many customer constraints and requirements, and huge number of customers in the target market have made QFDDMP a complex planning problem. Achieving optimal solution by which the products satisfy customers with lowest costing budget and minimum time requires employing House of Quality (HoQ). In this paper, by hybridizing the Gravitational Search Algorithm (GSA) as a local search technique and the Cuckoo Optimization Algorithm (COA) a new memetic algorithm (COGSA) is proposed and applied for solving QFDDMP. Using GSA part, COGSA can search more around best solutions found by COA and get more near to optimal. Comparing obtained results of COGSA, COA, genetic algorithm and particle swarm optimization has showed that COGSA is significantly stronger than other investigated algorithms in solving QFDDMP.