Abstract:
Many real world optimization problems involve multi-objectives. Multi-objective
problems are problem with two or more objectives and generally conflicting with
each other. Multi-objective optimization algorithms goals are converge to the set of
Pareto optimal solutions and maintain of diversity among Pareto optimal solutions.
Multi-objective optimization approaches can be divided into classical approaches
and evolutionary algorithms. Classical approaches generally convert multi-objective
function into single objective function and involve decision makers in the search.
Evolutionary optimization algorithms use a population based approach in which a set
of solutions evolves new solutions in the next generation. The use of population of
solutions helps to simultaneously find a set of Pareto optimal solution, thus making
evolutionary optimization computationally efficient. Genetic algorithm parameter is
the key factor to determine genetic algorithm performance.
