Andreas C. Nearchou

Solving the inverse kinematics problem of redundant robots operating in complex environments via a modified genetic algorithm

A new approach to solve the inverse kinematics problem of redundant robot manipulators in environments cluttered with obstacles is presented in this paper. The physical problem is formulated as an optimization problem under constraints, and solved via a modified genetic algorithm (mGA). The mGA searches for successive robot configurations in the entire free space so that the robot moves its end-effector from an initial placement to a final desired. The objective of this optimization is to simultaneously minimize the end-effectors positional error and the robots joint displacements. The efficiency of the proposed approach is demonstrated through multiple experiments carried out on several redundant robot manipulators. Comparison with two other approaches, the well known Pseudoinverse method and a previous method based on a simple GA, shows that the accuracy of the present solution is substantially better.

The effect of various operators on the genetic search for large scheduling problems

Abstract Genetic algorithms (GAs) have been applied on a variety of complex combinatorial optimization problems with high success. However, in relation to other classes of combinatorial problems, there is little reported experimental work for the application of GAs on large scheduling problems. The performance of a GA depends very much on the selection of the proper genetic operators. Crossover and mutation are the two major variation operators in any GA. This paper investigates the impact of various genetic operators on the genetic search through computational experiments carried out on the flow-shop scheduling problem (FSSP). A set of five crossover and six mutation operators are included in the experiments and their effectiveness on the overall performance of the GA process is measured, compared, and discussed. Furthermore, the case of crossover combination is examined under the FSSP framework investigating whether or not the various combinations outperform the sole usage of the best type of crossover operator.

A differential evolution approach for the common due date early/tardy job scheduling problem

The problem of scheduling multiple jobs on a single machine so that they are completed by a common specified date is addressed in this paper. This type of scheduling set costs depend on whether a job is finished before (earliness) or after (tardiness) the specified due date. The objective is to minimize a summation of earliness and tardiness penalty costs. Minimizing these costs pushes the completion time of each job as close as possible to the due date. The use of differential evolution as the optimization heuristic to solve this problem is investigated in this paper. Computational experiments over multiple (280 in total) public benchmark problems with up to 1000 jobs to be scheduled show the effectiveness of the proposed approach. The results obtained are of high quality putting new upper bounds to 60% of the benchmark instances.

Path planning of a mobile robot using genetic heuristics

A genetic algorithm for the path planning problem of a mobile robot which is moving and picking up loads on its way is presented. Assuming a findpath problem in a graph, the proposed algorithm determines a near-optimal path solution using a bit-string encoding of selected graph vertices. Several simulation results of specific task-oriented variants of the basic path planning problem using the proposed genetic algorithm are provided. The results obtained are compared with ones yielded by hill-climbing and simulated annealing techniques, showing a higher or at least equally well performance for the genetic algorithm.

Differential evolution for sequencing and scheduling optimization

This paper presents a stochastic method based on the differential evolution (DE) algorithm to address a wide range of sequencing and scheduling optimization problems. DE is a simple yet effective adaptive scheme developed for global optimization over continuous spaces. In spite of its simplicity and effectiveness the application of DE on combinatorial optimization problems with discrete decision variables is still unusual. A novel solution encoding mechanism is introduced for handling discrete variables in the context of DE and its performance is evaluated over a plethora of public benchmarks problems for three well-known NP-hard scheduling problems. Extended comparisons with the well-known random-keys encoding scheme showed a substantially higher performance for the proposed. Furthermore, a simple slight modification in the acceptance rule of the original DE algorithm is introduced resulting to a more robust optimizer over discrete spaces than the original DE.

Multi-objective fuzzy assembly line balancing using genetic algorithms

This paper presents a fuzzy extension of the simple assembly line balancing problem of type 2 (SALBP-2) with fuzzy job processing times since uncertainty, variability, and imprecision are often occurred in real-world production systems. The jobs processing times are formulated by triangular fuzzy membership functions. The total fuzzy cost function is formulated as the weighted-sum of two bi-criteria fuzzy objectives: (a) Minimizing the fuzzy cycle time and the fuzzy smoothness index of the workload of the line. (b) Minimizing the fuzzy cycle time of the line and the fuzzy balance delay time of the workstations. A new multi-objective genetic algorithm is applied to solve the problem whose performance is studied and discussed over known test problems taken from the open literature.

Multi-objective balancing of assembly lines by population heuristics

This paper is concerned with the solution of the multi-objective single-model deterministic assembly line balancing problem (ALBP). Two bi-criteria objectives are considered: 1. Minimising the cycle time of the assembly line and the balance delay time of the workstations. 2. Minimising the cycle time and the smoothness index of the workload of the line. A new population heuristic is proposed to solve the problem based on the general differential evolution (DE) method. The main characteristics of the proposed multi-objective DE (MODE) heuristic are: a. It formulates the cost function of each individual ALB solution as a weighted-sum of multiple objectives functions with self-adapted weights. b. It maintains a separate population with diverse Pareto-optimal solutions. c. It injects the actual evolving population with some Pareto-optimal solutions. d. It uses a new modified scheme for the creation of the mutant vectors. Moreover, special representation and encoding schemes are developed and discussed which adapt MODE on ALBPs. The efficiency of MODE is measured over known ALB benchmarks taken from the open literature and compared to that of two other previously proposed population heuristics, namely, a weighted-sum Pareto genetic algorithm (GA), and a Pareto-niched GA. The experimental comparisons showed a promising high quality performance for MODE approach.

A novel metaheuristic approach for the flow shop scheduling problem

Abstract Advances in modern manufacturing systems such as CAD/CAM, FMS, CIM, have increased the use of intelligent techniques for solving various combinatorial and NP-hard sequencing and scheduling problems. Production process in these systems consists of workshop problems such as grouping similar parts into manufacturing cells and proceeds by passing these parts on machines in the same order. This paper presents a new hybrid simulated annealing algorithm (hybrid SAA) for solving the flow-shop scheduling problem (FSSP); an NP-hard scheduling problem with a strong engineering background. The hybrid SAA integrates the basic structure of a SAA together with features borrowed from the fields of genetic algorithms (GAs) and local search techniques. Particularly, the algorithm works from a population of candidate schedules and generates new populations of neighbor schedules by applying suitable small perturbation schemes. Further, during the annealing process, an iterated hill climbing procedure is stochastically applied on the population of schedules with the hope to improve its performance. The proposed approach is fast and easily implemented. Computational results on several public benchmarks of FSSP instances with up to 500 jobs and 20 machines show the effectiveness and the high quality performance of the approach. In comparison to the performance of previous SA and GA methods, the performance of the proposed one was found superior.

Adaptive navigation of autonomous vehicles using evolutionary algorithms

Abstract Autonomous vehicles must be able to navigate freely in a constrained and unknown environment while performing a desired task. To increase its autonomy, a vehicle must be provided by sophisticated software navigators. Traditionally, navigators build a convenient model of the vehicles environment and plan feasible paths by reasoning about what actions must be performed to control the vehicle in that environment. This paper presents a genetic algorithm for adaptive navigation of a robot-like simulated vehicle. The proposed algorithm evolves feasible paths by performing an adaptive search on populations of candidate actions. The performance of the algorithm is demonstrated on problems with vehicles moving in two-dimensional grids and compared with that of a simple greedy algorithm and a random search technique.

A genetic path planning algorithm for redundant articulated robots

In some daily tasks, such as pick and place, the robot is requested to reach with its hand tip a desired target location while it is operating in its environment. Such tasks become more complex in environments cluttered with obstacles, since the constraint for collision-free movement must be also taken into account. This paper presents a new technique based on genetic algorithms (GAs) to solve the path planning problem of articulated redundant robot manipulators. The efficiency of the proposed GA is demonstrated through multiple experiments carried out on several robots with redundant degrees-of-freedom. Finally, the computational complexity of the proposed solution is estimated, in the worst case.