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Optimization technologies play a key role in addressing sophisticated optimization problems. However, with the increasing scale and dimension of problems, the performance of optimization technologies has been greatly challenged. To better address them, a new global optimization model is proposed inspired by the birefringence phenomenon, namely a birefringence learning (BRL) model which simulates the birefringence phenomenon of two refracted beams formed by the incident beam in the nature when the natural light enters some certain mediums. To substantiate the optimization performance of the model, it is applied to the artificial bee colony algorithm (ABC) to enhance its global optimization performance further and make its local optimization (exploitation) and global optimization (exploration) balance to some extent. In ABC, the BRL model as a mutation operator is employed in the phase of the scout bee to decrease the probability of ABC trapping into the local extreme region and then a novel ABC algorithm using the birefringence learning (NABC-BRL) is proposed. Via conducting abundant numerical experiments on some universally known benchmark functions, the experimental results and analysis indicate that it can obtain higher accuracy of solutions and faster convergence on majority of benchmark functions compared with some well-acknowledged algorithms, which also proves the effectiveness of global optimization of the BRL model. [ABSTRACT FROM AUTHOR]

Abstract: The Multicast Ad hoc On-Demand Distance Vector (MAODV) routing protocol is proposed for achieving multicast in a Mobile Ad hoc Network (MANET) while reducing bandwidth waste and energy power consumption. In MANET, packets transmission through a multicast tree may always have unreliable links caused by node mobility or lack of energy, and thus significantly degrades the performance. MAODV uses a broadcast-type local repair mechanism to find an alternative route to the multicast tree when some breaks happen on the tree. Although the local repair mechanism provides a specified time-to-live (TTL) to limit the repair range and the hop-count to the group leader, a large number of broadcast-type Route Request (RREQ) messages extensively yields control overhead and requires a large amount of power consumption to send control messages. Thus, this paper proposes a unicast-type multihop local repair protocol for multicast MANETs to recover lost links efficiently while achieving several advantages: increasing network reliability, increasing packet delivery rate, minimizing the number of control messages and reducing repair delay. Moreover, the optimal number of hops used in the multihop neighbor table is analyzed mathematically. Numerical results indicate that the proposed approach outperforms other repair approaches in terms of successful repair rate, control message overhead and packet delivery rate. [Copyright &y& Elsevier]

Abstract: In the stock market, technical analysis is a useful method for predicting stock prices. Although, professional stock analysts and fund managers usually make subjective judgments, based on objective technical indicators, it is difficult for non-professionals to apply this forecasting technique because there are too many complex technical indicators to be considered. Moreover, two drawbacks have been found in many of the past forecasting models: (1) statistical assumptions about variables are required for time series models, such as the autoregressive moving average model (ARMA) and the autoregressive conditional heteroscedasticity (ARCH), to produce forecasting models of mathematical equations, and these are not easily understood by stock investors; and (2) the rules mined from some artificial intelligence (AI) algorithms, such as neural networks (NN), are not easily realized. In order to overcome these drawbacks, this paper proposes a hybrid forecasting model, using multi-technical indicators to predict stock price trends. Further, it includes four proposed procedures in the hybrid model to provide efficient rules for forecasting, which are evolved from the extracted rules with high support value, by using the toolset based on rough sets theory (RST): (1) select the essential technical indicators, which are highly related to the future stock price, from the popular indicators based on a correlation matrix; (2) use the cumulative probability distribution approach (CDPA) and minimize the entropy principle approach (MEPA) to partition technical indicator value and daily price fluctuation into linguistic values, based on the characteristics of the data distribution; (3) employ a RST algorithm to extract linguistic rules from the linguistic technical indicator dataset; and (4) utilize genetic algorithms (GAs) to refine the extracted rules to get better forecasting accuracy and stock return. The effectiveness of the proposed model is verified with two types of performance evaluations, accuracy and stock return, and by using a six-year period of the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) as the experiment dataset. The experimental results show that the proposed model is superior to the two listed forecasting models (RST and GAs) in terms of accuracy, and the stock return evaluations have revealed that the profits produced by the proposed model are higher than the three listed models (Buy-and-Hold, RST and GAs). [Copyright &y& Elsevier]

Highlights: [•] We propose a novel chaos danger model immune algorithm (CDMIA). [•] Chaos perturbation and chaos regeneration are used for updating the antibody. [•] The chaos operator has effects on improving the population diversity. [•] CDMIA outperforms some optimization algorithms when considering some benchmarks. [•] CDMIA is suitable for global optimization. [ABSTRACT FROM AUTHOR]