The Nurse Rostering Problem is an NP-hard combinatorial optimization, scheduling problem for assigning a set of nurses to shifts per day by considering both hard and soft constraints. A novel metaheuristic technique is required for solving Nurse Rostering Problem (NRP). This work proposes a metaheuristic technique called Directed Bee Colony Optimization Algorithm using the Modified Nelder-Mead Method for solving the NRP. To solve the NRP, the authors used a multiobjective mathematical programming model and proposed a methodology for the adaptation of a Multiobjective Directed Bee Colony Optimization (MODBCO). MODBCO is used successfully for solving the multiobjective problem of optimizing the scheduling problems. This MODBCO is an integration of deterministic local search, multiagent particle system environment, and honey bee decision-making process. The performance of the algorithm is assessed using the standard dataset INRC2010, and it reflects many real-world cases which vary in size and complexity. The experimental analysis uses statistical tools to show the uniqueness of the algorithm on assessment criteria.

INTRODUCTION: Traveling Salesman Problem (TSP) is one of the vast research areas and has been considered as sub problems in many fields apart from computer science and also in the field of computer science.OBJECTIVES: This paper deals with the comparison of Ant System Ant System (AS) which is a variant of Ant Colony Optimization.METHODS: The performance of the Ant System is analysed by applying it on the Travelling Salesman Problem (TSP). The optimal results found on TSP using AS has been analysed with the elapsed time taken to find the optimal results, its mean, median, variance and the standard deviation.RESULTS: And also, the quality of solutions has been made by calculating the percentage of the optimality and the deviation of the solutions from the TSPLIB provides best known solutions. For instances, TSPLIB data sets have been used.CONCLUSION: Totally, 7 instances have been executed with three different set of parameters for AS and the results are analysed in terms of different parameter settings and performance metrics on each of it. The role of parameters has also been discussed along with the experimental results.

Multi objective Quadratic Assignment problem is an NP-Hard complex problem for assigning facilities to locations which significantly influence the transportation cost multitude in real-world applications like facilities location, distributed computing and data analysis. QAP can be solved using metaheuristic algorithm with deterministic and local search by equalising facilities and location. QAP can be applied to decision making framework, economic problems, scheduling to assign and determine services. This work portrays the exhaustive survey of quadratic assignment problem and various methodologies to solve QAP. A comprehensive study has been carried out to solve QAP using optimization algorithms. In this survey many approaches is examined by including multiple objective flow of facilities to locations by using evolutionary algorithm.