Combinatorial optimization problems are typically NP-hard, and thus very challenging to solve. In this paper, we present the random key cuckoo search (RKCS) algorithm for solving the famous Travelling Salesman Problem (TSP). We used a simplified random-key encoding scheme to pass from a continuous space (real numbers) to a combinatorial space. We also consider the displacement of a solution in ...

A rtificial bee Colony algorithm (ABC) is a population based heuristic search technique used for optimization problems. ABC is a very effective optimization technique for continuous optimization problem. Crossover operators have a better exploration property so crossover operators are added to the ABC. This paper presents ABC with different types of real coded crossover operator and its applica...

Congestion in large cities and populated areas is one of the major challenges in urban logistics, and should be addressed at different planning and operational levels. The Time Dependent Travelling Salesman Problem (TDTSP) is a generalization of the well known Traveling Salesman Problem (TSP) where the travel times are not assumed to be constant along the day. The motivation to consider the tim...

The generalized traveling salesman problem (GTSP) is an extension of the well-known traveling salesman problem. In GTSP, we are given a partition of cities into groups and we are required to find a minimum length tour that includes exactly one city from each group. The aim of this paper is to present a problem reduction algorithm that deletes redundant vertices and edges, preserving the value o...

The Generalized Traveling Salesman Problem (GTSP) is a well-known combinatorial optimization problem with a host of applications. It is an extension of the Traveling Salesman Problem (TSP) where the set of cities is partitioned into so-called clusters, and the salesman has to visit every cluster exactly once. While the GTSP is a very important combinatorial optimization problem and is well stud...

Abstract The ant colony optimization (ACO) is one efficient approach for solving the travelling salesman problem (TSP). Here, we propose a hybrid algorithm based on state-adaptive slime mold model and fractional-order system (SSMFAS) to address TSP. (SM) with two targeted auxiliary strategies emphasizes some critical connections balances exploration exploitation ability of SSMFAS. consideration...

Within the Artificial Intelligence community, there is great need for fast and accurate traversal algorithms, specifically those that find a path from a start to goal with minimum cost. Cost can be distance, time, money, energy, etc. Travelling salesman problem (TSP) is a combinatorial optimization problem. TSP is the most intensively studied problem in the area of optimization. Ant colony opti...

This report are described the versions and the synthesis results of new designs of electrically small antenna based on ant colony optimization algorithms. To study the parameters of the frame and non-loopback vibrators MMANA package was used. Geometric forms that were obtained might be used as contour lines of printed, slot antenna or as forming surface of the crystal dielectric resonator anten...

We describe a hybrid meta-heuristic algorithm for combinatorial optimization problems with a specific reference to the travelling salesman problem (TSP). The method is a combination of a genetic algorithm (GA) and greedy randomized adaptive search procedure (GRASP). A new adaptive fuzzy a greedy search operator is developed for this hybrid method. Computational experiments using a wide range of...

Genetic algorithm (GA) has several genetic operators that can be changed to improve the performance of particular implementations. These operators include selection, crossover and mutation. Selection is one of the important operations in the GA process. There are several ways for selection like Roulette-Wheel, Rank, and Tournament etc. This paper presents a new selection operator based on alpha...