# A Genetic Crow Search Algorithm for Optimization of Operation Sequencing in Process Planning
## 2 Related Work
Moreover, the tournament selection with the varying tournament size was included to prevent the algorithm from premature convergence and increased randomness. [PERSON] et al. [6] proposed the intelligent water drop algorithm in process planning optimization. A priority constraint matrix was used to generate feasible process plans. [PERSON] and [PERSON] [5] proposed a hybrid constrained permutation algorithm and genetic algorithm approach. A constrained permutation algorithm is employed to generate feasible operation sequences and a genetic algorithm is then used to search for an optimal solution within the feasible search space. A mixed crossover operator in the GA is used to avoid premature convergence to local optima. In a study by [PERSON] et al. [19], authors proposed the novel fairness-based transaction packing algorithm for permissioned blockchain empowered industrial IoT systems. A heuristic algorithm and a min-heap-based optimal algorithm were employed to solve the fairness problem. Since the fairness problem is related to the sum of weighting time of the selected transaction, it is transformed into the subset sum problem and the extensive experiments were conducted to verify the performance of the fairness-based transaction packing algorithm. In this paper, a novel improved metaheuristic approach called a genetic crow search algorithm (GCSA) is introduced for optimization of operation sequencing problem. The approach combines the classical CSA with the main components of the GA algorithm and through the joint action from local and global perspective of a population (flock) the evolution of crow individuals is achieved. The operation precedence graph (OPG) is utilized to represent the operation candidates, i.e., crow individuals with regard to corresponding precedence relationships among operations. Then, the initial population (flock) of crow individuals is constructed using vector-based representation to generate a group of feasible individuals. After the main steps of the CSA, a newly proposed nearest resource mechanism is applied to ensure the vectors of each crow individual are set to the nearest integer values. The evolution of the flock of crow individuals starts in the next stage, which consists of five strategies. Firstly, the tournament selection ensures that the most fit crow individuals from the flock are selected and the 3 SX crossover operator is applied to generate new crow individuals. The shift mutation strategy is then used to exchange random genes from crow individuals. The feasibility of these crow individuals is questioned, and in that sense, the constraint repair mechanism is used to ensure that infeasible solutions are converted to the feasible domain. The evolution of crow individuals ends with resource mutation, which performs minor changes in genes of machine, tool and TAD vectors, respectively. Using adaptive crossover and mutation probabilities, the exploration ability of the GCSA is improved. The input parameters of the algorithm are optimized using the Taguchi design of experiments, in order to obtain the optimized parameter settings. In each iteration of the GCSA approach, the position of crow individuals is evaluated using the fitness function and the memory of crow individuals is updated. To verify the performance of the proposed algorithm, two case studies with different level of dimensionality are considered. The computational results show that the proposed GCSA performs well and can be considered an efficient method for solving the operation sequencing problem. The rest of the paper is organized as follows. Section 2 describes the operation sequencing model by focusing on vector-based representation, data types and precedence constraints.