Mimetic Algorithm Research Articles

A New Hybrid Algorithm for Vehicle Routing Optimization

To solve the vehicle routing problem with simultaneous pickup–delivery and time windows (VRPSDPTW), a sine cosine and firefly perturbed sparrow search algorithm (SFSSA) is presented. Based on the standard sparrow search algorithm, the initial population uses tent chaotic mapping to change the population diversity; then, the discoverer location is updated using the sine cosine fluctuation range of the random weight factor, and finally the global population location is updated using the firefly perturbation strategy. In this study, SFSSA was compared with a genetic algorithm (GA), parallel simulated annealing algorithm (p-SA), discrete cuckoo search algorithm (DCS), and novel mimetic algorithm with efficient local search and extended neighborhood (MATE) adopting improved Solomon’s benchmark test cases. The computational results showed that the proposed SFSSA was able to achieve the current optimal solutions for 100% of the nine small-to-medium instances. For large-scale instances, SFSSA obtained the current optimal solutions for 25 out of 56 instances. The experimental findings demonstrated that SFSSA was an effective method for solving the VRPSPDTW problem.

Magnetic-resonance-based measurement of electromagnetic fields and conductivity in vivo using single current administration-A machine learning approach.

Diffusion tensor magnetic resonance electrical impedance tomography (DT-MREIT) is a newly developed technique that combines MR-based measurements of magnetic flux density with diffusion tensor MRI (DT-MRI) data to reconstruct electrical conductivity tensor distributions. DT-MREIT techniques normally require injection of two independent current patterns for unique reconstruction of conductivity characteristics. In this paper, we demonstrate an algorithm that can be used to reconstruct the position dependent scale factor relating conductivity and diffusion tensors, using flux density data measured from only one current injection. We demonstrate how these images can also be used to reconstruct electric field and current density distributions. Reconstructions were performed using a mimetic algorithm and simulations of magnetic flux density from complementary electrode montages, combined with a small-scale machine learning approach. In a biological tissue phantom, we found that the method reduced relative errors between single-current and two-current DT-MREIT results to around 10%. For in vivo human experimental data the error was about 15%. These results suggest that incorporation of machine learning may make it easier to recover electrical conductivity tensors and electric field images during neuromodulation therapy without the need for multiple current administrations.

Chaotic Evolutionary Programming for an Engineering Optimization Problem

The aim of the current paper is to present a mimetic algorithm called the chaotic evolutionary programming Powell’s pattern search (CEPPS) algorithm for the solution of the multi-fuel economic load dispatch problem. In the CEPPS algorithm, the exploration process is maintained by chaotic evolutionary programming, whereas exploitation is taken care off by a pattern search. The proposed CEPPS has two variants based on the Gauss map and the tent map. Seven generalized benchmark test functions and six cases of the multi-fuel economic load dispatch problem are considered for the performance analysis. It is observed from the analysis that the CEPPS solution procedure based on the tent map exhibits superiority to obtain an excellent solution and better convergence characteristics than traditional chaotic evolutionary programming. Further, the performance investigation for the considered economic load dispatch shows that the Gauss map CEPPS solution procedure performs better than the tent map based CEPPS to obtain the solution of the multi-fuel economic dispatch problem.

Improving efficiency for ensuring data group anonymity by developing an information technology

Widespread introduction of methods that ensure anonymity of information about individual groups (teams) of respondents in the field of official statistics is restrained by the lack of relevant industrial information technologies and systems. A three-level client-server architecture of an information technology providing data group anonymity was provided in which clients, application servers and databases are united into a local network to enhance security of primary data. A conceptual data model covering all key components of group anonymity was described. Implementation of the technology based on the Java Enterprise Edition 8 platform, Oracle GlassFish Server application server, MySQL database server and SciLab engineering calculations system wase considered. The information technology enables provision of group anonymity of data in the event of a threat of its violation by analyzing data of an auxiliary microfile. The technology provides operations for constructing fuzzy group models using a genetic algorithm and modification of a microfile with the help of a mimetic algorithm which enables effective provision of anonymity by introducing minor insignificant distortions into data. Application of the technology was illustrated by solution of the task of providing anonymity of a military group based on real data of American Society Survey, 2013. It was shown that solving the problem by a team of five specialists has enabled at least two and a half times faster the process of preparation of a microfile than by the use of an existing technology

A novel memetic ant colony optimization-based heuristic algorithm for solving the assembly line part feeding problem

In recent years, part feeding at assembly lines has become a critical issue as the result of a high level of product customization. The assembly line part feeding problem is a complex problem in which a number of decisions should be made in order to select the right quantity of each part to be supplied at the right time under a set of constraints. This study aims to cope with the part feeding problem at assembly lines by introducing a new memetic ant colony optimization-based heuristic algorithm. Due to the novelty of the problem and in order to evaluate the performance of the proposed memetic algorithm, a mathematical formulation is also presented. Case data from an automobile manufacturer and a set of generated instances are used to test both the mathematical model and the proposed memetic algorithm. The results reveal that although it is computationally difficult to solve the problem using the exact mathematical model, the mimetic algorithm was able to find sufficiently good solutions in a short period of time.

Mutation particle swarm optimization algorithm for solving the optimal operation model of thermal power plants

To solve joint optimization scheduling problem of thermal power plants based on the electricity market, the constraints and characteristics of optimal operation of thermal power plants are analyzed. From the perspective of economics and environmental science, we propose and establish an electricity market based joint optimization scheduling model of thermal power plants. Mutation particle swarm optimization algorithm (MPSO) is proposed to solve the model. Using an authentication instance, we compare and analyze the performances of the MPSO method and the mixed integer programming (MIP) method in solving the model. Further, considering a commonly used system, we show the efficiency of the MPSO method in solving the model; we apply two case studies to illustrate the performance of the MPSO method in solving the thermal unit commitment (UC) problem. To demonstrate the effectiveness of the MPSO method in solving the UC problem, we use MPSO method, PSO-LR (Lagrangian relaxation) [H. H. Balci and J. Valenzuela, Int. J. Appl. Math. Comput. Sci. 14(3), 411–421 (1989)], improved PSO [B. Zhao et al., Elect. Power Energy Syst. 28, 482–490 (2006)], mimetic algorithm [J. Valenzuela and A. Smith, J. Heuristics 8(2), 173–195 (2002)], adaptive LR [W. Ongsakul and N. Petcharaks, IEEE Trans. Power Syst. 19(1), 620–628 (2004)], stochastic priority list (PL) [T. Senjyu et al., Electr. Power Syst. Res. 76, 283–292 (2006)], PL based evolutionary algorithm [D. Srinivasan and J. Chazelas, in International Conference on Power System Technology (2004), Vol. 90, pp. 1746–1751], enhanced adaptive LR [W. Ongsakul and N. Petcharaks, IEEE Trans. Power Syst. 19(1), 620–628 (2004)], mixed-integer linear programming (MILP) [M. Carrin and J. M. Arroyo, IEEE Trans. Power Syst. 21(1), 321–332 (2006)], MILP [G. Morales-Espana et al., IEEE Trans. Power Syst. 28, 4897 (2013)], NBD [T. Niknam et al., Appl. Energy 86, 1667–1674 (2009)], generalized new benders decomposition approach (NBD) [S. Rahimi et al., World Appl. Sci. J. 6(12), 1665–1672 (2009)], LR and genetic algorithm[C. P. Cheng et al., IEEE Trans. Power Syst. 15(2), 707–714 (2000)], and seeded memetic algorithm (SMA) [J. Valenzuela and A. Smith, J. Heuristics 8(2), 173–195 (2002)] to solve the UC problem, simulation results are compared with the ones obtained by other methods. It is shown that the optimal operation model is right, the MPSO method is better than MIP method in solving the model, and they have universality. The effectiveness of the MPSO method in solving the UC problem is better than other methods. The MPSO method has better solutions in comparison with other methods, especially for systems with a large number of units.

Genetic algorithms to optimize base station sitting in WCDMA networks

In UMTS network, radio planning cannot only be based on signal predictions, but it must also consider the traffic distribution, the power control mechanism as well as the power limits and the signal quality constraints. The present work aims to optimize the number of base stations used in the WCDMA radio network. In this paper, we propose a mathematical programming model for optimizing the base station locations considered for the uplink (mobile to BS) direction, and which considers the typical power control mechanism of WCDMA. The two contrasting objectives of the optimization process are the traffic coverage maximization and the installation costs minimization. A mimetic algorithm (AM) (genetic algorithm+ a local search) is proposed to find good approximate solutions of this NP hard problem. The Numerical results are obtained for reels instances, and generated by using classical propagation models.

Population-Based Metaheuristic Approximation For Dosimetric Optimization in CyberKnife Radiation Therapy Treatment Planning

Population-Based Metaheuristic Approximation For Dosimetric Optimization in CyberKnife Radiation Therapy Treatment Planning

A Simulated Annealing Genetic Algorithm for the Electrical Power Districting Problem

Due to a variety of political, economic, and technological factors, many national electricity industries around the globe are transforming from non-competitive monopolies with centralized systems to decentralized operations with competitive business units. A key challenge faced by energy restructuring specialists at the World Bank is trying to simultaneously optimize the various criteria one can use to judge the fairness and commercial viability of a particular power districting plan. This research introduces and tests a new algorithm for solving the electrical power districting problem in the context of the Republic of Ghana and using a random test problem generator. We show that our mimetic algorithm, the Simulated Annealing Genetic Algorithm, outperforms a well-known Parallel Simulated Annealing heuristic on this new and interesting problem manifested by the deregulation of electricity markets.

Computerized analysis of epileptic activity and sleep in mobile long-term EEG monitoring.

Implemented on a LSI 11/73 microcomputer, an approach is presented to perform pattern recognition of one channel in high speed (factor 20). A mimetic algorithm, acting in time domain, continuously tests the signal with respect to the occurrence of spike-wave-like patterns and calculates a low frequency index describing the time of sleep. Those epochs suspected of containing paroxysmal activity are stored on disk for subsequent editing. In a similar way, 8-channel analysis is possible if the replay speed is less than 20 times real time.