Ant Colony Optimization Analysis

Ant Colony Optimization is another dominant swarm-based optimization technique often used to solve the ELD problem. The algorithm follows ant’s movement in search of food. The ant that reaches the food in shorter path returns to the nest earlier. Other ants in the nest have high probability of following the shorter route because pheromone deposited in shorter path is more than that deposited by ants traversing longer paths.

In ACO algorithm, a number of search trials, analogous to “ants”, work comparable to find the best solutions of the ELD problem. An ant develops a solution and shares the information (“pheromone”) with other ants. Though each ant can form a solution, improved solutions are established through this information exchange within an organizational zone.

S.Pothiya, I.Ngamroo and W.Kongprawechnon [19] has presented a solution of Economic Load Dispatch with non-smooth cost function using Ant Colony Optimization.

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It is influenced by the guidance of a teacher upon the outcome of learners in a class. It is a population based method and like other population based methods it uses a population of solutions to get the global solution. A group of learners create the population in TLBO. Every optimization method has numbers of different design variables. The various design variables in TLBO are denoted as different subjects offered to learners and the learners’ result in that particular subject is analogous to the ‘‘fitness’’, like in other population-based optimization methods. As per the society teacher is the most learned individual, the finest solution is equivalent to teacher in TLBO. The algorithm of TLBO has two portions. The first portion comprises of ‘‘teacher phase’’ and the second portion comprises of ‘‘learner phase’’. The ‘‘teacher phase’’ means learning from the teacher and the ‘‘learner phase’’ means learning through the communication among students in a