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In this work greedy comparison between particle swarm optimization and artificial bee colony algorithms was made using different test functions. Each algorithm was explained in detail, and the mathematical model behind the algorithms has been presented. It is found that particle swarm optimization is better than artificial bee colony and for a specific test function, artificial bee had failed to find a feasible solution. INTRODUCTIONPSO is a swarm intelligence optimization algorithm; it belongs to a class of optimization algorithms called meta-heuristics. PSO mimics the social behavior of animals like fish and birds, and it is a simple, powerful optimization algorithm. It was successfully applied to enormous applications in different fields of science and engineerings like machine learning, image processing, data mining, robotics and many others. Initially, PSO is introduced in 1995 by Russell Eberhart and James Kennedy [1]. They were working to develop a model describing the social behavior of animals like a flock of birds and school of fishes. Since 1995, though its simplicity, PSO has become one of the most useful and most popular algorithms to solve various optimization problems in various fields. The key point in this intelligence is the cooperation among those agents. Definitely, the level of the swarm intelligence cannot be reached by an individual unless it cooperates with another party [2]. In 2005, [3] has introduced a swarm intelligence optimization algorithm called artificial bee colony ABC. It is a metaheuristic algorithm that can be used to solve multi-dimensional optimization problems effectively. It mimics the foraging behavior of the honey bee colony and based on the model proposed by [4]. Artificial bee colony, states that there is a population of bees (agents) searching for the richer food source (best solution) in the neighborhood of the hive (search space). Every agent is a candidate solution and associated with only one particular solution in the search space. PSO ALGORITHMConsider Figure 1 which is the mathematical model behind the PSO MultiScience -XXXII. microCAD International Multidisciplinary Scientific Conference

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In this paper a detailed description of a method is presented to estimate the minimum structural dimensions of the robot arms. A comparative study is conducted between the harmony search and artificial bee colony algorithms in this scientific application. The comparison process was done through the kinematic equations of the serial robot manipulator to find the optimum lengths of links of the robot. A novel design for a seven-degrees-of-freedom robot arm was presented to conduct the comparative study on the presented optimization algorithms. This novel robot mimics the functionality of the SANDVIK robot arm for tunnelling works, but the presented type synthesis was designed to overcome the restrictions on the original SANDVIK arm.