Application of the invasive weed optimization algorithm to economic dispatch problems

The application of one rank cuckoo search algorithm for solving economic load dispatch problems

“…These methods can be classified into the groups including swarm intelligence, evolutionary algorithms, neural networks and hybrid algorithms. The swarm intelligence (SI) is the collective behavior of decentralized, self-organized, natural inspired systems consisting of particles swam optimization (PSO) [14,15], cuckoo search algorithm [16], gravitational search algorithm (GSA) [17], firefly algorithm (FA) [18], simulated annealing (SA) [19], bacterial foraging algorithm (BFA) [20], invasive weed optimization (IWO) [21], oppositional invasive weed optimization (OIWO) [22], modified artificial bee colony algorithm (MABCA) [23], harmony search (HS) [24] and biogeography-based optimization (BBO) [25]. Among these methods, SA is considered the less effective method since its optimal solutions are usually trapped in the local optimum rather than global optimum as well as it must suffer a difficult task for setting control parameters in addition to the long execution time.…”

Economic Emission Load Dispatch Problem with Valve-Point Loading Using a Novel Quasi-Oppositional-Based Political Optimizer

“…In this phase, each candidate improves its majority by considering aspects, namely, (1) candidates learning from their previous election, (2) influencing the voters with reference to the leader of the party and by himself, and (3) by performing the comparative analysis with the constituency winner. The campaigning of the candidate follows these three stages and updates his position either using (12) or (13) based on its relation with the previous position, i.e., if the fitness of the candidate is improved, then the position is updated using (12); otherwise, it is updated using (13) [26]. These are explained using the following two equations:…”

“…In this hybrid technique, the best particle's biased velocity vector is added by BFA random velocity to decrease the randomness during the search process and to increase the swarming. An invasive weed optimization technique is discussed to solve ELDP by considering prohibited operating zones and valve-point effects in [12]. A grey wolf optimizer (GWO)-based ELDP is discussed by application for small to large systems in [13].…”

An application of Gorilla troops optimizer in solving the problem of economic load dispatch considering valve point loading effect

“…In this hybrid approach, the BFA random velocity is added to the velocity vector of the best particle in order to reduce unpredictability throughout the search phase and boost swarming. A method for solving ELDP that considers forbidden operating zones, and valve-point effects is explored in [12]. For small to large systems, an ELD Problem based on a grey wolf optimizer (GWO) is addressed in [13].…”