Currently a standard technique to compute the heuristic in heuristic planning is to expand a planning graph on the relaxed problem. This paper presents a new approach to expand the planning graph, such that heuristic estimations are more accurate when an optimization metric criteria is given. Additionally, a new kind of Hill-Climbing search that combines two heuristics is proposed to deal with ...

Quadratic assignment problem (QAP) is a well-known problem in the facility location and layout. It belongs to the NP-complete class. There are many heuristic and meta-heuristic methods, which are presented for QAP in the literature. In this paper, we applied 2-opt, greedy 2-opt, 3-opt, greedy 3-opt, and VNZ as heuristic methods and tabu search (TS), simulated annealing, and pa...

Multidimensional scaling (MDS) which is a fundamental problem in data analysis, can be formulated as a nonlinear global optimization problem. In order to solve it, some local, heuristic and global optimization methods are implemented. In another hand, to perform our new heuristic approach, the MDS problem is studied by using a multistart Levenberg-Marquardt method and, also for some small examp...

augmented downhill simplex method (adsm) is introduced here, that is a heuristic combination of downhill simplex method (dsm) with random search algorithm. in fact, dsm is an interpretable nonlinear local optimization method. however, it is a local exploitation algorithm; so, it can be trapped in a local minimum. in contrast, random search is a global exploration, but less efficient. here, rand...

Heuristic optimization provides a robust and efficient approach for extracting approximate solutions of multi-objective problems because of their capability to evolve a set of non-dominated solutions distributed along the Pareto frontier. The convergence rate and suitable diversity of solutions are of great importance for multi-objective evolutionary algorithms. The focu...

Meta-heuristic algorithms inspired by the natural processes are part of the optimization algorithms that they have been considered in recent years, such as genetic algorithm, particle swarm optimization, ant colony optimization, Firefly algorithm. Recently, a new kind of evolutionary algorithm has been proposed that it is inspired by the human sociopolitical evolution process. This new algorith...

Meta-heuristic optimization algorithms are used in many application areas to solve problems. In recent years, meta-heuristic have gained importance over deterministic search solving However, none of the techniques equally effective all Therefore, researchers focused on either improving current or developing new ones. Many alternative inspired by nature been developed complex It is important com...

Parallel algorithms and systems have reached a complexity where it is usually di cult to optimize available degrees of freedom by hand. This paper presents a general method for optimizing parallel computations via heuristic guidance. Instead of developing such heuristics by hand, our method is based on automatic acquisition of appropriate heuristics. We will present the method in general terms ...

Today, Robotic is an essential technology from the entertainment to the industry. Thousands of articles have been published on Robotic. There are various types of robots such as parallel robots, industrial robots, mobile robots, autonomous mobile robots, health-care robots, military robots, entertainment robots, nano robots and swarm robots. So, this variety brings a lot of problems in Robotic....