Evolutionary Algorithms are search procedures based on natural selection. They have been used to solve a variety of single and multiple objective optimization problems. In single objective optimization problems, one seeks the solution that provides the best result of the objective function. In multiobjective optimization, one is faced with the problem of simultaneously optimizing a set of objective functions. To find a solution when dealing with multiple objectives, a notion of preference must be declared. Preference is used to determine when one solution dominates (or is better than) another solution.;A new type of evolutionary algorithm that has entered the scene is known as particle swarm. Particle swarm has been successfully used to solve a number of single objective optimization problems. However, there has been no application of particle swarm to multiobjective optimization. This research shows how particle swarm can be adapted for multiobjective optimization.;The problem under examination is the hardware/software codesign cosynthesis problem. Genetic algorithms, a type of evolutionary algorithm, have been successfully applied to this problem. This research also seeks to determine if there is a difference between the results of particle swarm and genetic algorithms on example instances of the cosynthesis problem.
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