The hybrid firefly algorithm with the fuzzy movement method for solving a complex scheduling problem

This paper proposes a hybrid algorithm that applied the firefly algorithm (FA) with the new idea labeled as the fuzzy movement method for solving a complex scheduling problem called the flexible job shop scheduling. The step of the proposed algorithm is similar to the original FA, which is based on the concept of flashing behavior to attract the other fireflies. In order to improve the efficiency of the FA algorithm for the FJSP, the proposed algorithm introduces three new ideas. In the first idea, the genetic algorithm (GA) is used to generate the high quality of the initial population. Next, the self-adaptive roulette wheel selection which embedded in the GA introduced to increase the diversity of the machine selection process. Finally, the fuzzy movement method is presented to enhance the work balancing ability between the high workload machines and the low workload machines. The proposed algorithm has been evaluated with a benchmark data set and compared to the other algorithm. The experimental results demonstrate that the proposed algorithm can effectively solve the flexible job shop scheduling problem.</jats:p

The Cluster Crossover Operation for The Symmetric Travelling Salesman Problem

This paper proposed the new algorithm intended to solve a specific real-world problem, the symmetric travelling salesman problem. The proposed algorithm is based on the concept of the galaxy based search algorithm (GbSA) and  embedded the new ideas called the clockwise search process and the cluster crossover operation. In the first step, the nearest neighbor algorithm introduces to generate the initial population. Then, the tabu list local search is employed to search for the new solution in surrounding areas of the initial population in the second step. The clockwise search process and the cluster crossover operation are employed to create more diversity of the new solution. Then, the final step, the hill climbing local search is utilized to increase the local search capabilities. The experiments with the standard benchmark test sets show that the proposed algorithm can be found the best average percentage deviation from the lower bound.</jats:p

A Hybrid Artificial Bee Colony Algorithm with Local Search for Flexible Job-shop Scheduling Problem

AbstractThis paper presents a hybrid artificial bee colony algorithm for solving the flexible job-shop scheduling problem (FJSP) with the criteria to minimize the maximum completion time (makespan). In solving the FJSP, we have to focus on two sub-problems: determining the sequence of the operations and selecting the best machine for each operation. In the proposed algorithm, first, several dispatching rules and the harmony search algorithm are used in creating the initial solutions. Thereafter, one of the two search techniques is randomly selected with a probability that is proportional to their fitness values. The selected search technique is applied to the initial solution to explore its neighborhood. If a premature convergence to a local optimum happens, the simulated annealing algorithm will be employed to escape from the local optimum. Otherwise, the filter and fan algorithm is utilized. Finally, the crossover operation is presented to enhance the exploitation capability. Experimental results on the benchmark data sets show that the proposed algorithm can effectively solve the FJSP

Memetic Algorithms for Business Analytics and Data Science: A Brief Survey

This chapter reviews applications of Memetic Algorithms in the areas of business analytics and data science. This approach originates from the need to address optimization problems that involve combinatorial search processes. Some of these problems were from the area of operations research, management science, artificial intelligence and machine learning. The methodology has developed considerably since its beginnings and now is being applied to a large number of problem domains. This work gives a historical timeline of events to explain the current developments and, as a survey, gives emphasis to the large number of applications in business and consumer analytics that were published between January 2014 and May 2018