HSA based solution to the UC problem

A Novel Strategy-Based Hybrid Binary Artificial Bee Colony Algorithm for Unit Commitment Problem

“…LR method provides a fast solution, but it may suffer from the numerical convergence and solution quality problems due to dual nature of the algorithm. Therefore, meta-heuristic methods such as genetic algorithm (GA) [11], evolutionary programming (EP) [12], memory simulated annealing (MSA) [13], simulated annealing (SA) [14], differential evolution (DE) [15], quantum inspired evolutionary algorithm (QEA) [16], bacterial foraging algorithm (BFA) [17], imperialistic competition algorithm (ICA) [18], particle swarm optimization (PSO) [19,20], harmony search algorithm (HSA) [21], binary gravitational search algorithm (BGSA) [22], modified differential evolution (MDE) [23] and invasive weed optimization (IWO) algorithm [24] have been proposed. These methods operate on the population of candidate solutions with different search mechanisms, which can easily handle the complicated time-dependent constraints and are claimed to have better optimal solutions in reasonable time.…”

“…To determine the optimum values for N 11 , N 10 and N 01 , the mathematical model is used, which is formulated using (19) and (21) as follows:…”

Multi‐objective unit commitment using search space‐based crazy particle swarm optimisation and normal boundary intersection technique

“…UC problem (UCP) utilises two basic decisions, namely the unit scheduling problem that determines ON/OFF status of the generating units in each time period of scheduling horizon and the economic load dispatch problem [1,2]. In the literature, various techniques have been proposed to solve the UCP, such as priority list [3], dynamic programming [4], mixed integer programming [5], branch and bound method [6], Lagrangian relaxation (LR) [7], genetic algorithm [8], bacteria foraging (BF) [9], particle swarm optimisation (PSO) [10,11], artificial bee colony (ABC) algorithm [12], harmony search algorithm (HSA) [13], self-adaptive bat-inspired algorithm (SABA) [14], second-order cone programming (SOCP) [15], evolutionary algorithms [16][17][18][19][20][21][22][23] and so on. The restructuring of power system has resulted in an open market environment [24,25] and therefore, the objective function of minimum production cost is changed to profit maximisation.…”

A modified binary artificial bee colony algorithm for ramp rate constrained unit commitment problem

“…LR method provides a fast solution but due to its dual nature, it may lead to slow convergence and suboptimal solutions. Therefore, metaheuristics techniques such as genetic algorithm (GA) , evolutionary programming (EP) , simulated annealing (SA) , particle swarm optimization (PSO) , differential evolution (DE) , bacterial foraging algorithm (BFA) , imperialistic competition algorithm (ICA) , harmony search algorithm (HSA) , binary gravitational search algorithm (BGSA) and invasive weed optimization have been proposed and are claimed to search near global optimal solution by satisfying complicated constraints easily. However, in order to obtain the global optimal solutions, computational time required by these methods increases for large‐scale UCP.…”