Quasi Opposition-Based Quantum Pieris Rapae and Parametric Curve Search Optimization for Real Power Loss Reduction and Stability Enhancement

In this paper Quasi Opposition-Based Quantum Pieris rapae optimization (QOQP) and Parametric Curve Search Optimization (PCSO) are modeled for resolving loss subsiding problem. Loss curtailing and minimizing the power aberration are the key objectives of the work. Quantum and quasi opposition has been integrated in Pieris rapae optimization to augment the quality of the search. Exploration procedure involves of repositioning functioning and Pieris rapae changeable operation. Quantum mechanics has been united with Pieris rapae optimization algorithm. In quantum process, topographies contend with the equivalent enactment with the definite period as they course in a reliable ground of medium. Quasi opposition-based learning is an enhanced version of Opposition based learning and it employs quasi-opposite points as an alternative of opposed points. Parametric curve search optimization algorithm engenders a new-fangled matrix at the commencement of iterations through arbitrarily designated patterns and it possesses multiple module mutation operators. Parametric curve search optimization algorithm also utilizes polynomials to yield the patterns which are mutated. In the proposed parametric curve search optimization algorithm approach mutation and crossover operators are architecturally modest, rapid, and exceptional which yields extremely effectual experimental patterns. Proposed quasi opposition-based quantum pieris rapae optimization (QOQP) and Parametric Curve Search Optimization (PCSO) is corroborated in 23 benchmark functions, Garver's 6-bus test system, IEEE 354 bus test system and Practical system - WDN 220 KV (Unified Egyptian Transmission Network (UETN)).