MSA Algorithm Research Articles

Combination model of urban tourism transportation based on nested logit model

ABSTRACT The traditional combination model based on multinomial logit is incapable of reflecting the nested structure of urban tourism destinations. Therefore, this paper established a variational inequality model of the combination of tourism demand distribution and transportation assignment based on nested logit. Through the first-order optimization conditions, it proved that the volume of travel distribution and transport assignment could meet the combination equilibrium conditions. Based on the MSA algorithm, it designed the solution algorithm of the model and verified the feasibility of the model and algorithm in a simplified tourism passenger transport network in Huangshan City. The calculation results show that the variational inequality model proposed in this paper can obtain the volume of travel distribution and transportation assignment at the same time, meanwhile compared with the multinomial logit, the nested logit structure fully considers the attraction measure of hotel and scenic spots, which is more in line with the choice behavior of tourists when choosing transportation route. In addition, the time value type of tourists has a great impact on the comprehensive tourism impedance which means that in the practical application, it is necessary to determine the time value type of tourists in each transportation routinereasonably.

A Study on Two-stage Selection Model of Tourism Destination at the Scale of Urban Agglomerations

Considering that the demand of tourism destination is variable on the scale of urban agglomeration, the selection process of travel destination is divided into two stages. The traditional transportation combination model based on the multinomial Logit cannot reflect this characteristic. And it is the lack of consideration of the influence of travel distribution and the dynamic transfer of passenger flow between various transport routes. Therefore, this thesis established a combination model of travel demand distribution and transportation assignment with two-stage terminal selection characteristics based on the nested Logit. Based on the analysis of tourists' trip process on the scale of urban agglomeration, a tourist flow transport network with travel destination nest structure is constructed. The generalized cost impedance function of transportation route is constructed based on the direct cost of transportation mode and the indirect cost of travel time. Based on the characteristics of two-stage destination selection of tourists, the form of travel distribution function of tourist flow is given. Through the first-order optimization conditions, it proved that the volume of travel distribution and tourism passenger transport assignment can meet the two-stage equilibrium conditions in the equilibrium state. Based on the idea of MSA algorithm, it designed the solution algorithm of the model and verified the feasibility of the model and algorithm in a simplified example. The calculation results show that the two-stage equilibrium assignment model proposed in this paper can obtain the volume of travel distribution and transportation assignment at the same time, meanwhile compared with the multinomial logit model, the nested Logit structure fully considers the attraction measure of the city destination and the scenic spot destination, which is more in line with the choice behavior of the tourists when choosing the transportation route. Thus, it provides a new comparable method for the optimal allocation of tourism passenger flow transport network resources on the scale of urban agglomeration, and can provide data support for the transportation organization plans of government decision-making departments and tourism transport enterprises.

Improved sine algorithm for global optimization

An improved sine cosine algorithm (abbreviated MSA) is presented to overcome the disadvantages of the sine cosine algorithm (SCA), such as its low accuracy, premature convergence, and slow local convergence. We changed the method for setting the conversion parameters of the SCA algorithm from a linear decline into a nonlinear decline in order to optimize the timing of global exploration and local exploration. In order to improve the convergence accuracy and to increase the convergence speed of the SCA, an inertia weight is introduced in the position update equation. Ten high-dimensional complex benchmark functions are simulated with five improved SCA algorithms, under the same particle numbers and maximum iteration times. According to our experiments, the MSA algorithm is not only much better than two of the improved SCA algorithms in optimization accuracy but also better than the other three improved SCA algorithms in terms of stability.

A Modified Method of Successive Approximations for Stochastic Recursive Optimal Control Problems

Based on the stochastic maximum principle for the partially coupled forward-backward stochastic control system (FBSCS for short), a modified method of successive approximations (MSA for short) is established for stochastic recursive optimal control problems. The second-order adjoint processes are introduced in the augmented Hamiltonian minimization step since the control domain is not necessarily convex. Thanks to the theory of bounded mean oscillation martingales (BMO martingales for short), we give a delicate proof of the error estimate and then prove the convergence of the modified MSA algorithm. In a special case, we obtain a logarithmic convergence rate. When the control domain is convex and compact, a sufficient condition which makes the control returned from the MSA algorithm be a near-optimal control is given for a class of linear FBSCSs.

Fast attribute selection based on the rough set boundary region

The problem of clustering exists in numerous fields such as bioinformatics, data mining, and the recognition of patterns. The function of techniques is to suitably select the best attribute from numerous contending attribute(s). RST-based approaches for definite data has gained significant attention, but cannot select clustering attributes for optimum performance. In this paper, the focus is on the processes that exhibit a similar degree of results to an identical attribute value. First, the MIA algorithm was identified as the supplement to the MSA algorithm, which experiences set approximation. Second, the proposition that MIA accomplishes lesser computational complexity through the indiscernibility relation measurement was highlighted. This observation is ascribed to the relationship between various attributes, which is markedly similar to those induced by others. Based on the fact that the size of the attribute domain is relatively small, the selection of such an attribute under such circumstances is problematic. Failure to choose the most suitable clustering attribute is challenging and the set is defined rather than computing the relative mean where it can only be implemented with a distinctive category of the information system, as illustrated with an example. Lastly, a substitute method for selecting a clustering attribute-based RST using Mean Dependency degree attribute(s) (MMD) was proposed. This involved selecting the maximum value of a mean attribute(s) as a clustering attribute through a considerable targeting procedure for the rapid selection of an attribute to settle the instability in selecting clustering attributes. Thus, the comparative performance of the selected clustering attributes-based RST techniques MSA and MIA was conducted.

Correlation analysis between OD demand and traffic flow in urban traffic network

Traffic congestion has become a serious problem in urban area. It may be alleviated by effectively controlling the external conditions affecting the traffic networks. In this paper, various traffic assignment methods are employed to analyze the correlation between OD demand and traffic flow in urban traffic network. To explore the effects of external conditions, three algorithms including all or nothing algorithm, Dial algorithm and MSA algorithm are applied for traffic networks. We study the sensitivity of traffic assignment methods with variation of external conditions (e.g. the number of road sections, the number of intermediate nodes, the number of OD pairs). The comparison results show that the degree of influences depends on OD demands. Additionally, the traffic congestion can be alleviated by removing the node or sections of traffic networks, which can be used to develop traffic management strategies.

Combining Pixel Swapping and Simulated Annealing for Land Cover Mapping.

Mixed pixels commonly exist in low-resolution remote sensing images, and they are the key factors hindering the classification of land covers and high-precision mapping. To obtain the spatial information at the subpixel level, subpixel mapping (SPM) technologies, including the pixel-swapping algorithm (PSA), that use the unmixed proportions of various land covers and allocate subpixel land covers have been proposed. However, the PSA often falls into a local optimum solution. In this paper, we propose a SPM method, the PSA_MSA algorithm, that combines the PSA and the modified simulated annealing algorithm to find the global optimum solution. The modified simulated annealing algorithm swaps subpixels within a certain range to escape the local optimum solution. The method also optimizes all the mixed pixels in a randomized sequence to further improve the mapping accuracy. The experimental results demonstrate that the proposed PSA_MSA algorithm outperforms the existing PSA-based algorithms for SPM. The images with different spatial dependences are tested and the results show that the proposed algorithm is more suitable for images with high spatial autocorrelation. In addition, the effect of proportion error is analyzed by adding it in the experiments. The result shows that a higher proportion error rate leads to larger degradation of the subpixel mapping accuracy. Finally, the performance of PSA_MSA algorithm with different ranges of selection on subpixel-swapping is analyzed.

Traffic assignment model for combined mode with travel condition constraints

Combined-mode traffic assignment is one of the key links for multi-modal transportation planning. In order to quantitatively evaluate the implementation effect of combined-mode transportation and explore its assignment mechanism, a combined-mode traffic assignment model with travel constraints is proposed. First, the practical factors of combined mode in real world are discussed and integrated into the analysis of multi-modal network transformation as related constraints. Second, the physical transportation network is translated to equivalent super network (SN) and state-augmented network (SAN) based on the graph theory. Moreover, the network size constrained by the actual conditions is conducive to further analysis of combined-mode trips. Third, a tri-level combined-mode traffic assignment model is formulated based on the simulation platform of SAN. The first level of the model is to address the combined-mode choice, the second level is to transfer choice on SAN, and the third level is to characterize auto travelers’ route choice behavior. By analyzing the impedance in multi-level network, an MSA algorithm solving Nest-Logit model is proposed. Finally, numerical example is performed to validate the model. The method proposed in this paper considers the travel similarity, reduces the network scale, conforms to the travel logic and makes up for the shortcomings of traditional traffic allocation methods.

Intercity Multi-modal Traffic Assignment Model and Algorithm for Urban Agglomeration Considering the Whole Travel Process

Based on the full analysis of the whole process of intercity travel of urban agglomerations, a super network model of urban agglomeration intercity multi-mode traffic system is constructed. The super network link is divided into travel link, transfer link, boarding link and alighting link, in which the travel link includes the travel link on the urban transportation network and the travel link on the intercity transportation network. Considering the factors of travel time, cost and degree of comfort, the generalized cost function and generalized hyper-path cost function are established for all links. On the basis of this, the stochastic equilibrium assignment model of urban agglomeration intercity multi-modal transportation system is proposed and the corresponding algorithm is designed based on MSA algorithm. Finally, an example is presented to illustrate the proposed method.

Multi-agent simulated annealing algorithm based on differential perturbation for protein structure prediction problems

Simulated annealing SA algorithm is extremely slow in convergence, and the implementation and efficiency of parallel SA algorithms are typically problem-dependent. To overcome such intrinsic limitations, this paper presents a multi-agent SA MSA algorithm to address protein structure prediction problems based on the 2D off-lattice model. Inspired by the learning ability of the mutation operators in differential evolution algorithm, three differential perturbation DP operators are defined to generate candidate solutions collaboratively. This paper also analyses the effect of different sampling grain, which determines how many dimensions will be perturbed when a candidate solution is generated. The proposed MSA algorithm can achieve better intensification ability by taking advantage of the learning ability from DP operators, which can adjust its neighbourhood structure adaptively. Simulation experiments were carried on four artificial Fibonacci sequences, and the results show that the performance of MSA algorithm is promising.

Multi-agent list-based threshold-accepting algorithm for numerical optimisation

Traditional list-based threshold-accepting LBTA algorithm is similar with simulated annealing SA algorithm, depends on an intense local search method, and utilises a list filling procedure with threshold values to search the solution space effectively. Inspired by the learning ability of particle swarm optimisation PSO, multi-agent LBTA MLBTA involves the learning knowledge to guide its sampling, explores the solution space in a co-evolution mode. Compare with multi-agent SA MSA algorithm adapting the same local search version, MLBTA incorporates a dynamic list of threshold values which is adapted according to the topology of the solution space and tunes only one parameter. Dispense with sophisticated parameters as MSA, MLBTA balances the intensification and diversification iteratively. Computational results on functions optimisation and protein structure prediction PSP problems show that MLBTA algorithm achieves better or comparable performances with MSA.

Analysis of learning-based multi-agent simulated annealing algorithm for function optimisation problems

Canonical simulated annealing SA algorithm is extremely slow in convergence, and the implementation and efficiency of parallel SA algorithms are typically problem-dependent. Multi-agent SA MSA algorithms, which use learned knowledge to guide its sampling, can overcome such intrinsic limitations naturally. Learning strategy, which decides the representation, selection, and usage of knowledge, may affect the performance of MSA algorithms significantly. Using the current population as learned knowledge, we design three different knowledge selection schemes, selecting from better agents, selecting from worse agents and selecting from all agents randomly, to select knowledge to guide sampling. A differential perturbation operator is designed to generate candidate solution from the selected knowledge. Comparison was carried on four widely used benchmark functions, and the results show that learning-based MSA algorithm has good performance in terms of convergence speed and solution accuracy. Furthermore, simulation results also show that even learning from worse agents significantly outperforms not learning at all.

Design of LDPC Decoder Based on CMMB

In this paper, design of a LDPC decoder in CMMB is presented. LDPC decoding algorithms for CMMB are analyzed and the optimal decoding algorithm-Normalized MSA are used to implement the decoder, and the algorithm is simulated to determine the design parameters. A partial parallel architecture based on Normalized MSA algorithm is proposed, and the architecture is simulated with a fixed-point model to determine the best quantification scheme for initial information and intermediate data format.

Stochastic user equilibrium with combined mode in a degradable multi-modal transportation network

This paper proposes a stochastic user equilibrium assignment model for ascertaining the difference in the travel mode choice behavior in a degradable multi-modal traffic network. In the proposed model, on the basis of the actual travel behavior which often needs to transfer different traffic modals, all the travels are categorized into two classes: pure car trip travelers and combined mode trip(car-subway) travelers. The multi-modal traffic network equilibrium conditions are formulated as an equivalent fixed-point problem, and the proof of its solution existence is provided afterwards. A heuristic solution algorithm MSWA method which is the extension of the classical MSA method is introduced for solving this fixed-point problem. An example is used to illustrate the application of the proposed model and solution algorithm. Moreover, the performance comparison of the MSA and MSWA algorithm is also given to prove the precision and efficiency of MSWA method for solving this problem.

An Optimal Supply Policy for Multi-product Multi-retailer Using Simulated Annealing Method

We consider a multi-product multi-retailer supply and inventory system. The total costs which include inventory cost and transport cost belong to the supplier. The transport cost consists of a major carrier cost and a minor delivery cost for each product. The total costs saving can be achieved by coordinating the supply of a group of products. Applying for each individual product a cyclic supply and inventory policy, where the delivery cycle time is a multiple of basic cycle time, can reduce the total costs. A modified simulated annealing method (MSA) to determine the optimal supply policy is proposed in this paper. A numerical example presents that the MSA is feasible. Experiment results show that the MSA algorithm significantly outperforms other solution methods in the quality of the solution as well as in the running time.

A demand model with departure time choice for within-day dynamic traffic assignment

A within-day dynamic demand model is formulated, embodying, in addition to the classic generation, distribution and modal split stages, an actual demand model taking into account departure time choice. The work focuses on this last stage, represented through an extension of the discrete choice framework to a continuous choice set. The dynamic multimodal supply and equilibrium model based on implicit path enumeration, which have been developed in previous work are outlined here, to define within-day dynamic elastic demand stochastic multimodal equilibrium as a fixed point problem on users flows and transit line frequencies. A MSA algorithm capable, in the case of Logit route choice models, of supplying equilibrium flows and frequencies on real dimension networks, is presented, as well as the specific procedures implementing the departure time choice and actual demand models. Finally, the results obtained on a test network are presented and conclusions are drawn.

The Multiple Stack Algorithm Implemented on a Zilog Z-80 Microcomputer

A sequential algorithm for decoding convolutional codes, the multiple stack algorithm or MSA devised by Chevillat and Costello, has been implemented and tested on a Zilog Z-80 microcomputer system. The results of this implementation, when compared to those of a (2, 1)8 Viterbi algorithm implemented on the same microcomputer system, show that the MSA obtains significantly higher data throughput. The results of the MSA implementation of a (2, 1)15 code suggest that the MSA is a practical choice for codes having longer constraint lengths than are normally used with Viterbi decoding. The comparative results may serve as guidelines for future implementation of the MSA algorithm on faster, more capable microprocessors.