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A bee colony optimization (BCO) algorithm for traveling salesman problem (TSP) is presented in this paper. The BCO model is constructed algorithmically based on the collective intelligence shown in bee foraging behaviour. Experimental results comparing the proposed BCO model with some existing approaches on a set of benchmark problems are presented.
Many real world industrial applications involve finding a Hamiltonian path with minimum cost. Some instances that belong to this category are transportation routing problem, scan chain optimization and drilling problem in integrated circuit testing and production. This paper presents a bee colony optimization (BCO) algorithm for traveling salesman problem(More)
In a bee colony, bees perform waggle dance in order to communicate the information of food source to their hive mates. This foraging behaviour has been adapted in a Bee Colony Optimization (BCO) algorithm together with 2-opt local search to solve the Traveling Salesman Problem (TSP) [1]. To reduce the high overhead incurred by 2-opt in the BCO algorithm(More)
Scheduling is a crucial activity in semiconductor manufacturing industry. Effective scheduling in its operations leads to improvement in the efficiency and utilization of its equipment. Job Shop Scheduling is an NP-hard problem which is closely related to some of the scheduling activities in this industry. This paper presents an improved Bee Colony(More)
Combinatorial Optimization Problems (COPs) appear in various types of industrial applications. Finding an optimum solution for COPs with large scale of data, constraints and variables is NP-hard. This paper proposed a generic Bee Colony Optimization (BCO) framework for COPs that mimics the foraging process and waggle dance performed by bees. The framework(More)
Keywords: Intelligent Water Drops (IWD) Swarm-based optimization Ranking-based selection methods Feature selection (FS) Rough set (RS) Multiple knapsack problem (MKP) Travelling salesman problem (TSP) a b s t r a c t The Intelligent Water Drop (IWD) algorithm is a recent stochastic swarm-based method that is useful for solving combinatorial and function(More)
The Asymmetric Traveling Salesman Problem (ATSP) is one of the Combinatorial Optimization Problems that has been intensively studied in computer science and operations research. Solving ATSP is NP-hard and it is harder if the problem is with large scale data. This paper intends to address the ATSP using an hybrid approach which integrates the generic Bee(More)
Sequential Ordering Problem (SOP) is a type of Combinatorial Optimization Problem (COP). Solving SOP requires finding a feasible Hamiltonian path with minimum cost without violating the precedence constraints. SOP models myriad of real world industrial applications, particularly in the fields of transportation, vehicle routing and production planning. The(More)
Bees perform waggle dance in order to communicate the information of food source to their hive mates. This unique foraging behaviour has been computationally realized as an algorithmic tool named the Bee Colony Optimization (BCO) algorithm to solve different types of Combinatorial Optimization Problems such as Traveling Salesman Problem (TSP). In order to(More)