A New Quantum-Inspired Binary PSO: Application to Unit Commitment Problems for Power Systems

Abstract

Abstract-This paper proposes a new binary particle swarm optimization (BPSO) approach inspired by quantum computing, namely quantum-inspired BPSO (QBPSO). Although BPSO-based approaches have been successfully applied to the combinatorial optimization problems in various fields, the BPSO algorithm has some drawbacks such as premature convergence when handling heavily constrained problems. The proposed QBPSO combines the conventional BPSO with the concept and principles of quantum computing such as a quantum bit and superposition of states. The QBPSO adopts a Q-bit individual for the probabilistic representation, which replaces the velocity update procedure in the particle swarm optimization. To improve the search capability of the quantum computing, this paper also proposes a new rotation gate, that is, a coordinate rotation gate for updating Q-bit individuals combined with a dynamic rotation angle for determining the magnitude of rotation angle. The proposed QBPSO is applied to unit commitment (UC) problems for power systems which are composed of up to 100-units with 24-h demand horizon. Index Terms-Binary particle swarm optimization, combinatorial optimization, constraint treatment technique, quantum computing, quantum evolutionary algorithm, unit commitment.

Bare bones particle swarm optimization with adaptive chaotic jump for feature selection in classification

“…To verify the performance of the proposed BBPSO-ACJ, the following 8 EC based wrappers are employed: Genetic algorithm (GA) 40 , PSO 31 , Binary PSO (BPSO) 30 ,Binary PSO with chaotic inertia weight (BPSO-CI) 35 , BBPSO 23 , Quantum inspired PSO (QBPSO) 41 , Binary PSO with catfish effect (BPSO-CE) 15 , PSO (4-2) 18 . Furthermore, two filter based methods, linear forward selection (LFS) and greedy stepwise based selection (GSBS), are also employed for comparison.…”

A binary ABC algorithm based on advanced similarity scheme for feature selection

“…The other goal is to put forward a comprehensive comparative study of some variants of the ABC, PSO and GA algorithms on wrapper feature selection in terms of the classification performance and the feature subset size for the future studies of researchers. To establish the second goal, seven algorithms, which are binary PSO (BPSO) [22], new velocity based binary PSO (NBPSO) [23], quantum inspired binary PSO (QBPSO) [24], discrete ABC (DisABC) [21], angle modulated ABC (AMABC) [25], modification rate based ABC (MRABC) [26] and genetic algorithms (GA) [27] are employed, and 10 benchmark datasets, including various classes, instances and features are chosen from the UCI machine learning repository [28]. To our knowledge, the employed algorithms except for BPSO and GA are the first time to be used in feature selection, and a comprehensive comparative analysis on feature selection is not very common in the literature.…”

“…Yun-Won et al [24] integrated the concept and principles of quantum computing, including quantum bit and superposition of states into basic BPSO, namely quantum inspired BPSO (QBPSO). In QBPSO, a Q-bit individual is integrated as the probability of particles taking value 1 and 0 instead of the velocity.…”

“…In the experiments, the following parameters are used: the number of individuals in the population is set to 30 as in [4]; the maximum number of iterations is empirically set to 50; the parameters of BPSO are selected as in [69] such that c 1 = 2, c 2 = 2, initial weight= 0.9 and V max = 6; the crossover and mutation rates of GA are selected as 0.8 and 0.2 as in [70]; the parameters of NBPSO are selected as in [23] such that w 1 = 0.5 and Vmax = 6; the parameters of QBPSO are selected as in [24] such that Qmax = 0.05 and Qmin = 0.01 ; the limit parameter of the AMABC, MRABC, DisABC and MDisABC is experimentally chosen as 50; max and min are set to 0.9 and 0.5 in MDisABC and DisABC as in [21]; the MR parameter of MRABC is set to 0.5; and the CR parameter of MDisABC is experimentally chosen as 0.25.…”

Enhanced priority list unit commitment method for power systems with a high share of renewables

“…Each system is composed of the appropriate number of copies of the original 10 unit system, 5 with the demand profile scaled accordingly. This test system, originally set up in [13], has been used extensively in the literature, to test a wide range of algorithms, see, e.g., [4,7,8,10,13,[17][18][19][20][21][22][23][24][25][26][27][28][29]. In this benchmark case, only upward reserves are considered (consistent with the literature).…”