FLC control for tuning exploration phase in bio-inspired metaheuristic

Growing popularity of the Bat Algorithm has encouraged researchers to focus their work on its further improvements. Most work has been done within the area of hybridization of Bat Algorithm with other metaheuristics or local search methods. Unfortunately, most of these modifications not only improves the quality of obtained solutions, but also increases the number of control parameters that are needed to be set in order to obtain solutions of expected quality. This makes such solutions quite impractical. What more, there is no clear indication what these parameters do in term of a search process. In this paper authors are trying to incorporate Mamdani type Fuzzy Logic Controller (FLC) to tackle some of these mentioned shortcomings by using the FLC to control the exploration phase of a bio-inspired metaheuristic. FLC also allows us to incorporate expert knowledge about the problem at hand and define expected behaviors of system – here process of searching in multidimensional search space by modeling the process of bats hunting for their prey

Tractor and Semitrailer Routing Problem of Highway Port Networks under Unbalanced Demand

In China, highway port networks are essential in carrying out tractor and semitrailer transportation operations. To analyze the characteristics of tractor and semitrailer routing in highway port networks, this study examined the situation in which the demands at both ends of the operation might be unbalanced and multiple requirements might be raised in the operation of tractor and semitrailer transportation. An optimal tractor and semitrailer routing model for an entire network was established to reduce the total transportation costs and the number of towing vehicles in the network. Moreover, a heuristic algorithm was designed to solve the model. The comparisons of Strategy 1 and Strategy 2 for a two-stage network swap trailer show that the number of pure network swaps trailer tractors decreases by 21.6% and 18.6%, respectively; and that the cost drops by 7.8% and 7.9%, respectively. In other words, swap trailer transport enterprises can abandon the original swap trailer transportation mode for a two-stage network and adopt a routing optimization strategy for an entire network to achieve superior operation performance, reduce costs, and enhance profits. The study provides a reference for optimizing tractor and semitrailer routing in highway port networks with balanced and multiple demands

New directional bat algorithm for continuous optimization problems

Bat algorithm (BA) is a recent optimization algorithm based on swarm intelligence and inspiration from the echolocation behavior of bats. One of the issues in the standard bat algorithm is the premature convergence that can occur due to the low exploration ability of the algorithm under some conditions. To overcome this deficiency, directional echolocation is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities. In addition to such directional echolocation, three other improvements have been embedded into the standard bat algorithm to enhance its performance. The new proposed approach, namely the directional Bat Algorithm (dBA), has been then tested using several standard and non-standard benchmarks from the CEC’2005 benchmark suite. The performance of dBA has been compared with ten other algorithms and BA variants using non-parametric statistical tests. The statistical test results show the superiority of the directional bat algorithm

Forecasting model for short-term wind speed using robust local mean decomposition, deep neural networks, intelligent algorithm, and error correction

Wind power generation has aroused widespread concern worldwide. Accurate prediction of wind speed is very important for the safe and economic operation of the power grid. This paper presents a short-term wind speed prediction model which includes data decomposition, deep learning, intelligent algorithm optimization, and error correction modules. First, the robust local mean decomposition (RLMD) is applied to the original wind speed data to reduce the non-stationarity of the data. Then, the salp swarm algorithm (SSA) is used to determine the optimal parameter combination of the bidirectional gated recurrent unit (BiGRU) to ensure prediction quality. In order to eliminate the predictable components of the error further, a correction module based on the improved salp swarm algorithm (ISSA) and deep extreme learning machine (DELM) is constructed. The exploration and exploitation capability of the original SSA is enhanced by introducing a crazy operator and dynamic learning strategy, and the input weights and thresholds in the DELM are optimized by the ISSA to improve the generalization ability of the model. The actual data of wind farms are used to verify the advancement of the proposed model. Compared with other models, the results show that the proposed model has the best prediction performance. As a powerful tool, the developed forecasting system is expected to be further used in the energy system

A NOVEL METAHEURISTIC ALGORITHM: DYNAMIC VIRTUAL BATS ALGORITHM FOR GLOBAL OPTIMIZATION

A novel nature-inspired algorithm called the Dynamic Virtual Bats Algorithm (DVBA) is presented in this thesis. DVBA is inspired by a bat’s ability to manipulate frequency and wavelength of the emitted sound waves when hunting. A role based search has been developed to improve the diversification and intensification capability of standard Bat Algorithm (BA). Although DVBA is inspired from bats, like BA, it is conceptually very different from BA. BA needs a huge number of population size; however, DVBA employs just two bats to handle the ”exploration and exploitation” conflict which is known as a real challenge for all optimization algorithms. Firstly, we study bat’s echolocation ability and next, the most known bat-inspired algorithm and its modified versions are analyzed. The contributions of this thesis start reading and imitating bat’s hunting strategies with different perspectives. In the DVBA, there are only two bats: explorer and exploiter bat. While the explorer bat explores the search space, the exploiter bat makes an intensive search of the local with the highest probability of locating the desired target. Depending on their location, bats exchange the roles dynamically. The performance of the DVBA is extensively evaluated on a suite of 30 bound-constrained optimization problems from Congress of Evolutionary Computation (CEC) 2014 and compared with 4 classical optimization algorithm, 4 state-of-the-art modified bat algorithms, and 5 algorithms from a special session at CEC2014. In addition, DVBA is tested on supply chain cost problem to see its performance on a complicated real world problem. The experimental results demonstrated that the proposed DVBA outperform, or is comparable to, its competitors in terms of the quality of final solution and its convergence rates.Epoka Universit

Adaptive Bat Algorithm Optimization Strategy for Observation Matrix

Bat algorithm, as an optimization strategy of the observation matrix, has been widely used. Observation matrix has a direct impact on the reconstructed signal accuracy as a projection transformation matrix, and it has been widely used in various algorithms. However, for the traditional experimental process, randomly generated observation matrices often result in a larger reconstruction error and unstable reconstruction results. Therefore, it is a challenge to retain more feature information of the original signal and reduce reconstruction error. To obtain a more accurate reconstruction signal and less memory space, it is important to select an effective compression and reconstruction strategy. To solve this problem, an adaptive bat algorithm is proposed to optimize the observation matrix in this paper. For the adaptive bat algorithm, we design a dynamic adjustment strategy of the optimal radius to improve its global convergence ability. The results of our simulation experiments verify that, compared with other algorithms, it can effectively reduce the reconstruction error and has stronger robustness