An adaptive quantum-inspired differential evolution algorithm for 0

Abstract

-Differential evolution (DE) is a population based evolutionary algorithm widely used for solving multidimensional global optimization problems over continuous spaces. However, the design of its operators makes it unsuitable for many real-life constrained combinatorial optimization problems which operate on binary space. On the other hand, the quantum inspired evolutionary algorithm (QEA) is very well suitable for handling such problems by applying several quantum computing techniques such as Q-bit representation and rotation gate operator, etc. This paper extends the concept of differential operators with adaptive parameter control to the quantum paradigm and proposes the adaptive quantum-inspired differential evolution algorithm (AQDE). The performance of AQDE is found to be significantly superior as compared to QEA and a discrete version of DE on the standard 0-1 knapsack problem for all the considered test cases.Keywords-differential evolution; quantum inspired evolutionary algoithm; 0-1 knapsack problem; quantum computing

A Binary Cuckoo Search Big Data Algorithm Applied to Large‐Scale Crew Scheduling Problems

“…The application of quantum differential evolution is also observed in the knapsack problem [53], combinatorial problems [54], and methods of image thresholding [55]. A quantum algorithm using cuckoo search metaheuristic was applied to the knapsack problem [56] and bin packing problem [57].…”

“…Two quantum binarization applications to the knapsack problem are reported previously using harmony search in [58] and monkey algorithm in [59]. The quantum differential evolution algorithm was applied to the knapsack problem in [53], combinatorial problems [54], and image threshold methods in [55]. Using the cuckoo search metaheuristic, a quantum algorithm was applied to the knapsack problem [56] and bin packing problem [57].…”

Putting Continuous Metaheuristics to Work in Binary Search Spaces

“…The quantum method has been applied to a swarm optimization algorithm in combinatorial optimization [109], cooperative approach [110], knapsack problems [108], and power quality monitor [111]. In differential evolution, it has been applied to knapsack problems [112], combinatorial problems [113], and image threshold methods [114]. The cuckoo search metaheuristic has been used for 0-1 knapsack problems [115] and the bin packing problem [116].…”

A binary differential evolution algorithm learning from explored solutions

“…For numerical comparisons, BLDE is compared with the angle modulated particle swarm optimization (AMPSO) [20], the angle modulated differential evolution (AMDE) [21], the dissimilarity artificial bee colony (DisABC) algorithm [13], the binary particle swarm optimization (BPSO) algorithm [16], the binary differential evolution (binDE) [9] algorithm and the self-adaptive quantum-inspired differential evolution (AQDE) algorithm [12]. As is suggested by the designers of the algorithms, the parameters of AMPSO, AMDE, DisABC, BPSO, binDE, and AQDE, are listed in Table 3.…”