A new metaheuristic method applied to the global path planning for mobile robots in dynamic environments is presented. This algorithm, named the Quad Harmony Search method, consists of dividing the robot‟s environment into free regions by applying the Quad-tree algorithm and utilizing this information to accelerate the next phase which implements the Harmony Search optimization method to provid...

The traveling salesman problem (TSP) is one of the most important issues in combinatorial optimization problems that are used many engineering sciences and has attracted attention scientists researchers. In this issue, a starts to move from desired node called warehouse returns starting place after meeting n customers provided each customer only met once. aim issue determine cycle with minimum ...

Metaheuristic optimization algorithms present an effective method for solving several problems from various types of applications and fields. Several metaheuristics evolutionary have been emerged recently in the literature gained widespread attention, such as particle swarm (PSO), whale algorithm (WOA), grey wolf (GWO), genetic (GA), gravitational search (GSA). According to literature, no one m...

response surface methodology is a common tool in optimizing processes. it mainly concerns situations when there is only one response of interest. however, many designed experiments often involve simultaneous optimization of several quality characteristics. this is called a multiresponse surface optimization problem. a common approach in dealing with these problems is to apply desirability funct...

Software pipelining is a compile-time scheduling technique that overlaps successive loop iterations to achieve instruction-level parallelism. It allows us to hide memory latency by overlapping the prefetches for a future iteration with the computation of the current iteration. This paper presents an efficient algorithm for determining the iteration bound of cyclic data flow graphs and the optim...

Most optimization problems in real life applications are often highly nonlinear. Local optimization algorithms do not give the desired performance. So, only global optimization algorithms should be used to obtain optimal solutions. This paper introduces a new nature-inspired metaheuristic optimization algorithm, called Hoopoe Heuristic (HH). In this paper, we will study HH and validate it again...

Multidimensional 0-1 Knapsack Problem (MKP) is a well-known integer programming problems. The objective of MKP is to find a subset of items with maximum value satisfying the capacity constraints. A Memetic algorithm on the basis of Design and Implementation Methodology for Metaheuristic Algorithms (DIMMA) is proposed to solve MKP. DIMMA is a new methodology to develop a metaheuristic algorithm....

Metaheuristic algorithms are becoming an important part of modern optimization. A wide range of metaheuristic algorithms have emerged over the last two decades, and many metaheuristics such as particle swarm optimization are becoming increasingly popular. Despite their popularity, mathematical analysis of these algorithms lacks behind. Convergence analysis still remains unsolved for the majorit...

Metaheuristic algorithms are well-known optimization tools which have been employed for solving a wide range of optimization problems. Several extensions of differential evolution have been adopted in solving constrained and nonconstrained multiobjective optimization problems, but in this study, the third version of generalized differential evolution (GDE) is used for solving practical engineer...