Integrating genetic algorithms and tabu search for unit commitment problem

Abstract

Optimization is the art of obtaining optimum result under given circumstances. In design, construction and maintenance of any engineering system, Engineers have to take many technological and managerial decisions at several stages. The ultimate goal of all such decisions is to either maximize the desired benefit or to minimize the effort or the cost required. This paper shows a memetic algorithm, a real coded Genetic Algorithm combined with local search, synergistically combined with Tabu Search is effective and efficient for solving large Unit Commitment (UC) problems in electrical power systems. A set of feasible generator schedule is first formulated by real coded genetic algorithm method. Then these pre-committed schedules are optimized by ordinary local search and tabu search. The tabu search based hybrid GT algorithm was tested on reported UC problems previously addressed by some existing techniques such as Dynamic Programming (DP), and Simple Genetic Algorithms. Numerical results for systems up to 36 units are given and commented on. A comprehensive unit commitment software package is developed. The future scope of the paper using the proposed method finds a place in the conclusion.

A binary-real-coded differential evolution for unit commitment problem

“…For numerical experimentation, a set of six instances of the UCP are considered from the literature, in which the numbers of units are 10, 20, 40, 60, 80 and 100 over a 24-h time horizon. The reason for considering these particular instances is that these are the most widely studied instances for illustrating the performances of different solution techniques proposed for the UCP [1][2][3][4][5]7,16,[19][20][21]26,29,31,32,36,[38][39][40][41][42]44,45]. The unit related known data for the 10-unit power system are given in Table 2 with notations as per Eqs.…”

“…Apart from these, some hybrid methods combining metaheuristics with deterministic methods or other metaheuristics are also investigated in order to reduce the search space in large-scale UCP. Such hybrid methods include LR and GA [5], LR and MA [42], LR and PSO [3], GA and SA [30], and GA and TS [36].…”

Adaptive bacterial foraging and genetic algorithm for unit commitment problem with ramp rate constraint

“…The performance of HABFGA algorithm without considering the ramp rate constraint is compared with genetic algorithms and tabu search [40], Lagrangian relaxation and genetic algorithms [41], differential evolution [42], ant colony [43], shuffled frog-leaping algorithm [44] and binary real-coded genetic algorithm (BRGA) [3]. Table IV shows the results of this comparison that shows that the HABFGA algorithm yields enhanced results over other published methods.…”

Genetic Algorithm Solution of the Knapsack Problem Used in Finding Full Issues in the Holy Quran Based on the Number (19)

“…A genetic algorithm is an algorithm that simulates the evolution of living organisms in a manner to contribute to the enhancement of computerized systems (Sudhakaran & Raj, 2010). Therefore, genetic algorithms are considered to be "Bio-Inspired" applications (Stein et al, 2004).…”