In planar robot control, the singularity problem is frequently encountered when determining geometric or kinematic model. To overcome this problem, a new method presented in paper. Indeed, used to compute joint positions of 3R robots with high accuracy. The main idea proposed use metaheuristic algorithm for retrieving coordinates four reference points object image that captured by hand-eye came...

A metaheuristic is generally a procedure designed to find a good solution to a difficult optimization problem. Known optimization search metaheuristics heavily rely on parameters, which are usually introduced so that the metaheuristic follows some supposedly related to the optimization problem natural process (simulated annealing, swarm optimization, genetic algorithms). Adjusting the parameter...

Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this paper, the author extends the standard firefly algorithm furth...

Nature-inspired metaheuristic algorithms, especially those based on swarm intelligence, have attracted much attention in the last ten years. Firefly algorithm appeared in about five years ago, its literature has expanded dramatically with diverse applications. In this paper, we will briefly review the fundamentals of firefly algorithm together with a selection of recent publications. Then, we d...

the traveling salesman problem (tsp) is a well-known combinatorial optimization problem and holds a central place in logistics management. the tsp has received much attention because of its practical applications in industrial problems. many exact, heuristic and metaheuristic approaches have been proposed to solve tsp in recent years. in this paper, a modified ant colony optimization (maco) is ...

Stochastic flexible flow shop scheduling problem (SFFSSP) is one the main focus of researchers due to the complexity arises from inherent uncertainties and also the difficulty of solving such NP-hard problems. Conventionally, in such problems each machine’s job process time may encounter uncertainty due to their relevant random behaviour. In order to examine such problems more realistically, fi...

The importance of high performance algorithms for tackling difficult optimization problems cannot be understated, and in many cases the most effective methods are metaheuristics. When designing a metaheuristic, simplicity should be favored, both conceptually and in practice. Naturally, it must also lead to effective algorithms, and if possible, general purpose ones. If we think of a metaheurist...