Abstract: Imperialist Competitive Algorithm (ICA) is a recent
meta-heuristic method that is inspired by the social evolutions for
solving NP-Hard problems. The ICA is a population-based algorithm
which has achieved a great performance in comparison to other metaheuristics.
This study is about developing enhanced ICA approach to
solve the Cell Formation Problem (CFP) using sequence data. In
addition to the conventional ICA, an enhanced version of ICA,
namely EICA, applies local search techniques to add more
intensification aptitude and embed the features of exploration and
intensification more successfully. Suitable performance measures are
used to compare the proposed algorithms with some other powerful
solution approaches in the literature. In the same way, for checking
the proficiency of algorithms, forty test problems are presented. Five
benchmark problems have sequence data, and other ones are based on
0-1 matrices modified to sequence based problems. Computational
results elucidate the efficiency of the EICA in solving CFP problems.
Abstract: This paper addresses minimizing the makespan of the
distributed permutation flow shop scheduling problem. In this
problem, there are several parallel identical factories or flowshops
each with series of similar machines. Each job should be allocated to
one of the factories and all of the operations of the jobs should be
performed in the allocated factory. This problem has recently gained
attention and due to NP-Hard nature of the problem, metaheuristic
algorithms have been proposed to tackle it. Majority of the proposed
algorithms require large computational time which is the main
drawback. In this study, a general variable neighborhood search
algorithm (GVNS) is proposed where several time-saving schemes
have been incorporated into it. Also, the GVNS uses the sophisticated
method to change the shaking procedure or perturbation depending
on the progress of the incumbent solution to prevent stagnation of the
search. The performance of the proposed algorithm is compared to
the state-of-the-art algorithms based on standard benchmark
instances.