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The Traveling Salesman Problem (TSP) is well-known established scheduling problems. We propose a novel method for the TSP using the divide-and-conquer strategy. We employ K-means algorithm to find the city clustering and then solve a sequence of sub-city in a given order by Particle Swarm Optimization (PSO). The PSO is modified by incorporating genetic algorithm operators, namely mutation, so that...
The Knapsack Problems (KPs) is a well-known combinatorial optimization problem. It has a variety of practical applications. We propose the algorithm to solve both 0–1 Knapsack problem (KP) and Multidimensional Knapsack Problem (MKP) by fusing the Binary Particle Swarm Optimization (BPSO) and Simulated Annealing (SA) with maximum profit objective. The main contribution is to develop a novel approach...
The Resource-Constrained Project Scheduling Problem (RCPSP) is a classical well-known and NP-hard problem which includes the resource and precedence constraints that has been applied to many applications. This paper proposes the Radius Particle Swarm Optimization (RPSO) to solve the RCPSP. It extends the Particle Swarm Optimization (PSO) by regrouping the agent particles within the appropriate radius...
Particle Swarm Optimization (PSO) is a swarm intelligence based and stochastic algorithm to solve the optimization problem. Nevertheless, the traditional PSO has disadvantage from the premature convergence when finding the global optimization. To prevent from falling into the local optimum, we propose the Radius particle swarm optimization (R-PSO) which extends the Particle Swarm Optimization by regrouping...
Optimization of cutting operations is an active area of research in CNC-based manufacturing. The limited capabilities of the CAD/CAM systems require development of a new software and new numerical methods verified by practical machining. We formulate the problem of tool-path optimization in terms of interpolation of the required part surface in the curvilinear coordinate system associated with the...
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