This work proposes an attempt to control the navigation of a wall following mobile robot using a hybrid metaheuristic which is a combination of two different population-based heuristics such as Gravitational Search (GS) and Particle Swarm Optimization (PSO). The hybrid metaheuristic is called GSPSO which is used to train a multi-layered Artificial Neural Network (ANN) with the available benchma...

The Traveling Salesman Problem (TSP) is a classical NPhard combinatorial problem that has been intensively studied through several decades. A large amount of literature is dedicated to this problem. TSP can be directly applied to some real life problems, and to be generalized to some other set of important combinatorial problems. Nowadays, a large collection of TSP instances can be found such a...

Controlled tabular adjustment is an SDL methodology based on a mixed integer linear programming model. We develop new hybrid heuristics and new metaheuristic learning approaches for solving this model, and examine their performance. Our new approaches are based on partitioning the problem into its discrete and continuous components, and first creating a hybrid that reduces the number of binary ...

The metaheuristics are approximation methods which deal with difficult optimization problems. The Work that we present in this paper has primarily as an objective the adaptation and the implementation of two advanced metaheuristics which are the Memetic Algorithms (MA) and the Electromagnetism Metaheuristic (EM) applied in the production systems of Hybrid Flow Shop (HFS) type for the problem of...

Metaheuristic algorithm is one of the most popular methods in solving many optimization problems. This paper presents a new hybrid approach comprising of two natures inspired metaheuristic algorithms i.e. Cuckoo Search (CS) and Accelerated Particle Swarm Optimization (APSO) for training Artificial Neural Networks (ANN). In order to increase the probability of the egg’s survival, the cuckoo bird...

This paper proposes a two-phase metaheuristic approach to planning daily farm work for agriculture production corporations. The two-phase metaheuristic contains the optimization of resources assignment and searching schedule based on Genetic Algorithm and hybrid Petri nets model. In the experiment, the effect on optimizing the resource assignment and priority list, initializing population of GA...

Metaheuristic algorithms are often nature-inspired, and they are becoming very powerful in solving global optimization problems. More than a dozen of major metaheuristic algorithms have been developed over the last three decades, and there exist even more variants and hybrid of metaheuristics. This paper intends to provide an overview of nature-inspired metaheuristic algorithms, from a brief hi...

This paper presents three hybrid metaheuristic algorithms that further improve the two hybrid differential evolution (DE) metaheuristic algorithms described in Liao [1]. The three improved algorithms are: (i) MDE′–HJ, which is a modification of MA–MDE′ in Liao [1] by replacing the random walk with direction exploitation local search with the Hooke and Jeeves (HJ) method; (ii) MDE′–IHS–HJ, which...

This paper presents a hybrid metaheuristic algorithm (HMA) for Multi-Mode Resource-Constrained Project Scheduling Problem (MRCPSP) in PERT networks. A PERT-type project, where activities require resources of various types with random duration, is considered. Each activity can be accomplished in one of several execution modes and each execution mode represents an alternative combination of resou...