a study on the performance of wolf colony optimization method

In recent years there is an enormous increase in the emergence of non-deterministic search methods. the effective way of animals in problem-solving (like discovering the shortest path to the food source) has been examined by scientists and swarm intelligence has become a research field that imitates the behavior of animals in swarm. wolf colony optimizer (wco), particle swarm optimizer (pso), bat algorithm (bo), ant colony optimizer (aco), glowworm swarm optimizer (gso), artificial bee colony optimizer (abc), and firefly optimizer (ffo) are some of the swarm intelligence based non-deterministic methods. in the present study, the seven methods above are investigated separately. five mathematical functions are resolved individually by these seven methods. each function has its own design variables and restraints. ten thousand iterations carried out for each function. the performances of these optimization methods are evaluated and compared within each function individually. performances of algorithms over convergence are compared by plotting convergence rate graph of methods for each function.