TY - JOUR

T1 - Novel operators for quantum evolutionary algorithm in solving timetabling problem

AU - Tayarani, Mohammad

PY - 2020/8/13

Y1 - 2020/8/13

N2 - Timetabling is a well-known combinatorial optimization problem which belongs to the class of NP hard problems. Quantum Evolutionary Algorithms (QEA) are highly suitable for the class of combinatorial optimization problems, but since proposed, they have not been used in solving this problem. In this paper we develop new operators for QEA to improve its performance in solving timetabling problem. The first operator we develop is a reinitialization operator which checks the population and when it is converged, reinitializes it to maintain the diversity. The other operator is called the Diversity Preserving operator which monitors the q-individuals, and if it finds more than one q-individuals searching around the same local optimum, reinitializes some of them to make sure different q-individuals are exploiting different regions in the search space. In this paper we also study the population size and the population structure of QEA in solving timetabling problem. In order to test the proposed algorithm, we perform experiments and compare the proposed algorithm to the existing algorithms on some well-known benchmark functions.

AB - Timetabling is a well-known combinatorial optimization problem which belongs to the class of NP hard problems. Quantum Evolutionary Algorithms (QEA) are highly suitable for the class of combinatorial optimization problems, but since proposed, they have not been used in solving this problem. In this paper we develop new operators for QEA to improve its performance in solving timetabling problem. The first operator we develop is a reinitialization operator which checks the population and when it is converged, reinitializes it to maintain the diversity. The other operator is called the Diversity Preserving operator which monitors the q-individuals, and if it finds more than one q-individuals searching around the same local optimum, reinitializes some of them to make sure different q-individuals are exploiting different regions in the search space. In this paper we also study the population size and the population structure of QEA in solving timetabling problem. In order to test the proposed algorithm, we perform experiments and compare the proposed algorithm to the existing algorithms on some well-known benchmark functions.

U2 - 10.1007/s12065-020-00438-0

DO - 10.1007/s12065-020-00438-0

M3 - Article

JO - Evolutionary Intelligence

JF - Evolutionary Intelligence

ER -