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Vehicle routing problem is an important combinatorial optimization problem. It has an important position in logistics optimization and supply chain management theory. Due to traffic flow, traffic incidents and other factors, the travel speed and travel time of road has large time-variability and randomness in real transport network. The study of vehicle routing problem in time-dependent network has...
Ant Colony Optimization (ACO) has proven to be a very powerful optimization heuristic for combinatorial optimization problems. This paper introduces a new type of ACO algorithm that will be used for routing along multiple routes in a network as opposed to optimizing a single route. Contrary to traditional routing algorithms, the Ant Dispersion Routing (ADR) algorithm has the objective of determining...
Genetic programming approaches have previously been employed in the literature to evolve heuristics for various combinatorial optimisation problems. This paper presents a hyper-heuristic genetic programming methodology to evolve more sophisticated one dimensional bin packing heuristics than have been evolved previously. The heuristics have access to a memory, which allows them to make decisions with...
An important challenge within hyper-heuristic research is to design search methodologies that work well, not only across different instances of the same problem, but also across different problem domains. This article conducts an empirical study involving three different domains in combinatorial optimisation: bin packing, permutation flow shop and personnel scheduling. Using a common software interface...
In this paper, we consider a combinatorial optimization problem of scheduling n jobs of block type on linearly aligned m identical machines. Each job Jj is characterized by four integers, an arrival time aj , a processing time pj, the number qj of consecutive machines required by the job (hence, each job Jj can be represented by a rectangular block with the size of pj × qj in a geometrical interpretation),...
The combinatorial optimization occurs in many real-world problems including the fields of engineering, physics and economics. It has been recognized that some problems with highly degenerate states are difficult to solve in terms of many existing optimization algorithms. This paper proposes a novel stochastic method with modified extremal optimization (EO) and nearest neighbor search to deal with...
The collection vehicle routing problems with intermediate facilities (CVRP-IF) is actually belong to a well-known generalization of VRP, the Multi-Depot Vehicle Routing Problem with Inter-Depot Routes (MDVRPI), which is a combinatorial optimization problem and holds a central place in reverse logistics management, such as waste collection management. This paper presents an improved multiple ant colony...
Dynamic Alliance Partner Selecting problem is generally modeled as a combination optimization problem, the popular solution of this problem is building math models and choosing proper optimization method. Researchers have already brought forward many useful models and algorithms, but till now no solution of Partner Selection Problem is public accepted due to the problem's complexion, which means there...
Vehicle routing problem(VRP) is an NP-hard problem, Ant colony algorithm is an effective tool for solving combinatorial optimization problems like VRP. On the base of understanding VRP problem and Ant Colony Algorithm (ACA), analysis ACA's application in VRP, for its shortcomings, reference MMAS thought, introduce dynamic negative feedback mechanism and appropriate increase the visibility mechanism,...
Ant colony algorithm (ACA) is a heuristic search algorithm to solve combinational optimization problems whose selection strategy has direct relations with the information content of the routes which is indefinite. Based on comparison of several improved algorithms, improved ACA based on comentropy is proposed in the paper. By controlling comentropy figure, route selection and the probability of local...
The network reconfiguration optimal control in distribution networks is modeled as a multi-objective combinational optimization. Multiple objectives are considered for load balancing among the feeders, minimum deviation of the nodes voltage, minimize the power loss and branch current constraint violation. Based on the objectives evaluated by membership functions respectively. These objectives are...
This paper presents a novel hybrid approach for solving the Container Loading (CL) problem based on the combination of caving degree (CD) algorithm and variable neighborhood descent (VND) algorithm. More precisely, the first constructive phase is based on a caving degree (CD) heuristic developed for the single container loading problem. In the second improvement phase, four new moves are designed...
We present a novel parallel auction algorithm implementation for solving the linear sum assignment problem. It is implemented using the message passing interface (MPI) on a computer cluster. Our approach enables dynamic computational load balancing over all processors throughout all steps of the algorithm's execution. We show that the performance of our approach is superior to existing approaches...
This paper deals with solving large instances of the Linear Sum Assignment Problems (LSAPs) under realtime constraints, using Graphical Processing Units (GPUs). The motivating scenario is an industrial application for P2P live streaming that is moderated by a central tracker that is periodically solving LSAP instances to optimize the connectivity of thousands of peers. However, our findings are generic...
Hyper-heuristics are increasingly used in function and combinatorial optimization. Rather than attempt to solve a problem using a fixed heuristic, a hyper-heuristic approach attempts to find a combination of heuristics that solve a problem (and in turn may be directly suitable for a class of problem instances). Hyper-heuristics have been little explored in data mining. Here we apply a hyper-heuristic...
The problem of estimating the three-dimensional (3D) geometry of an object from a sequence of images obtained at different focus settings is called shape from focus (SFF). The conventional SFF methods apply focus measure operator at each pixel using neighboring pixels in the same image frame. However, for an object with complex geometry, such methods cannot compute accurate focus level of a pixel,...
A Covering Array denoted by CA(N; t,k,??) is a matrix of size N ?? k, where each tuple of t columns has at least one time each of the vt combinations of symbols. The C As are combinatorial objects used for software testing and design of experiments in: biology, agriculture, medicine, etc. CAs can be constructed using heuristic algorithms, greedy search and algebraic procedures. The Hartman Style Rising...
The resource-constrained project scheduling problem (RCPSP) is a typical combinatorial optimization problem. Base on the general model of ant colony algorithm for solving the RCPSP, this paper presents a new 2opt called PC-2opt which guarantees precedence constraints between activities. PC-2opt, which needn't to calculate the location of successors, could be directly used to solve the RCPSP, and improve...
Partner selection is a classical combinatorial optimization problem. Its solution is a list of nodes which are the least value of each link. When the links and candidates are increased continuously, the complexity of partner selection grows exponentially. It is difficult to solve that problem in method of exhaustion. So this paper puts forward an improved algorithm, namely partner selection ant colony...
One of the most challenging problems in computational biology is the reconstruction of DNA sequences from DNA fragments. This paper describes the problems of sequencing by hybridization with standard, isothermic and multistage oligonucleotide libraries. However, the problems are NP-hard in the strong sense in case of errors. With the study of combinatorial optimization, it has become common for the...
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