Multi-objective Bat Algorithm Research Articles

Artificial intelligence based modelling and hybrid optimization of linseed oil biodiesel with graphene nanoparticles to stringent biomedical safety and environmental standards

In this work, experiments were carried out in line with Design of Experiments (DOE) standards to assess the performance and emission features of 5% graphene nanoparticles added linseed biodiesel. The engine was operated with the blends of B10, B20, and B30 with 5% graphene nano additives (designated as B10G5, B20G5, and B30G5). To find the parameter's optimum values, the Desirability Function approach (DFA), Swarm Salp single objective, Multi Objective Bat algorithm (MOBA), Response surface methodology (RSM) and D-optimal design approach were employed. Advanced machine learning (ML) techniques were employed to anticipate these characteristics. It was found that B20G5 had a better brake thermal efficiency (BTE), when compared to the other samples (and around 11% higher than diesel fuel at full load). The emissions of Carbon monoxide (CO) and Hydrocarbon (HC) were lower for B20G5 blended fuel than for diesel (Around 23.52% lower than diesel). In comparison to Response surface methodology (RSM), the overall coefficient of determination (R2) value using Artificial Neural Network (ANN) for was high. As a result, it was revealed that the ANN was typically better than the RSM in forecasting the various factors affecting the engine performance. The optimum outcomes were achieved by single objective (Salp Swarm algorithm) and multi-objective algorithms. According to multi-objective algorithm, a B20G5 nano additive biodiesel mix at its maximum Brake power (BP) produced the highest value of BTE with the lowest Nitrogen Oxides (NOx) emissions. The comparison shows that B20G5 can be used easily without making any modifications to engines.

Optimization of a solar-based PEM methanol/water electrolyzer using machine learning and animal-inspired algorithms

This contribution scrutinizes the performance of the integrated photovoltaic thermal (PVT) system with the proton exchange membrane methanol electrolyzer (PEMME) and water electrolyzer (PEMWE) as sustainable ways to produce hydrogen. Artificial neural networks (ANNs), are adopted to evaluate the effect of various operating parameters on the performance of the systems. The adopted ANNs are radial basis function (RBF), extreme learning machine (ELM), long short-term memory (LSTM), and gated recurrent unit (GRU). Moreover, to obtain the optimum performances of the systems, the multi-objective whale optimization algorithm (MOWOA) and multi-objective bat algorithm (MOBA) are implemented. It is found that solar radiation is the most influential parameter on the hydrogen production rate of the systems, followed by ambient temperature, inlet temperature, working fluid mass flow rate, and wind speed. According to the machine learning outputs, the highest hydrogen production rate for the PVT-PEMWE and PVT-PEMME systems are around 2.45 mol h−1 m−2 and 5.44 mol h−1 m−2, respectively. Moreover, the MOBA shows that the hydrogen production rate and electrical efficiency of the PVT-PEMWE and PVT-PEMME systems are 2.29 mol h−1 m−2 and 18.74%, and 5.06 mol h−1 m−2, and 18.77%, respectively, at their global optimum point by considering hydrogen production rate and electrical efficiency as the objective functions. This study reveals that although at the same working conditions, the electrical and thermal output of the water-based PVT system is superior to that of the water/methanol-based PVT system, the PVT-PEMME system outperforms the PVT-PEMWE system from the hydrogen production viewpoint, due to the higher efficiency of the PEM methanol electrolyzer.

A MOBA-Root-MUSIC-Based Demodulation Method for Surface Acoustic Wave Torque Sensor

To improve the measurement accuracy of surface acoustic wave (SAW) torque sensor, an ultrahigh precision SAW torque sensor demodulation method based on multi-objective bat algorithm (MOBA) and root-MUSIC algorithm was proposed. The root-MUSIC algorithm was adopted to construct the polynomial equation of SAW echo signals. The construction of the objective function was based on two principles: the least distance to unit circle and least phase angle to the mean roots. MOBA was used to optimize the objective function to improve the accuracy of the roots. The numerical simulation and actual interrogating experiments were carried out and comparisons with other methods were conducted. The demodulation results verified that the proposed method had a minimum estimation error with standard deviation of 1 Hz. The designed demodulation method was used to the SAW torque measurement system. The torque sensing SAW resonator (SAWR) was designed using an FEM software and manufactured with a frequency of 433.945 MHz and an unload <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${Q}$ </tex-math></inline-formula> factor of 13 370. The SAWR was glued on a bearing to measure applied torque. The torque loading and unloading experiments demonstrated that the nonlinearity of the proposed method was 0.1402%, which is the minimum when compared to other methods. The hysteresis was 1.1791%, and the sensitivity was 1.3570 kHz/Nm. Numerical simulation and experimental results demonstrated the effectiveness and accuracy of the designed algorithm in SAW torque sensor demodulation system.

Optimization of Shearer Drum Based on Multi-Objective Bat Algorithm with Grid (MOBA/G)

The shearer drum undertakes the main function of coal falling and loading, and picks distributed on it have a great impact on the performance of the drum. However, few studies have optimized the pick and drum at the same time. In this paper, parameters of pick and drum are considered as design variables, and the response functions of design variables are established based on the central composite experiment method. The optimal structural and working parameters of the pick and the drum of MG500/1130-WD shearer are obtained by using the multi-objective bat algorithm and multi-objective bat algorithm with grid, respectively. Comparing results of the two algorithms, the multi-objective bat algorithm with grid is more effective in improving the comprehensive performance of the drum. According to the optimized design variables, a coal mining test is carried out to verify the optimization effect of the algorithm. The result provides some theoretical references for the design and production of the drum and has some engineering application value.

Spectral and energy efficiency trade-off in massive MIMO systems using multi-objective bat algorithm

Abstract The rise in the usage of wireless communication increases the cellular communication by the same rate. With the continuation of this situation, the density in data traffic has the potential to cause problems in the near future. Coping with spectral efficiency-energy efficiency trade-off using massive MIMO systems is considered to be a reasonable solution to this problem. In this paper, cellular communication simulations were performed in cases with different number of users, number of antennas and transmission power of massive MIMO systems and then non-dominated solutions are determined. Multi-objective bat algorithm has been used to make this process much shorter. At last stage, performance of this algorithm is compared with various intelligent optimization algorithms and with ideal non-dominated solutions. When the algorithms are compared with each other, it is seen that multi-objective bat algorithm has the best performance among them.

Multi-objective optimization of fixture locating layout for sheet metal part using Kriging and MOBA

Owing to the multi-objective attribute and excessive computational burden of finite element analysis (FEA) during the optimization of fixture locating layout, a new multi-objective optimization method for the sheet metal fixture design based on the Kriging surrogate model and multi-objective bat algorithm (MOBA) is proposed in this paper. The fixture locating layout is treated as the design variable while the overall deformation and maximum deformation of sheet metal under its dead weight are treated as the objective functions. The training and testing sample sets are generated by Latin hypercube sampling (LHS) and FEA. Kriging surrogate model is built by learning from the training sample set and the multi-objective optimization model for fixture locating layout is constructed based on the established Kriging surrogate model. MOBA is employed to find Pareto optimal solution set for the sheet metal fixture locating layout. Finally, a bi-objective optimization case of fixture locating layout for the sheet metal part based on the “4-2-1” locating principle is conducted to verify the proposed method.

Cyber Intrusion Detection System Based on a Multiobjective Binary Bat Algorithm for Feature Selection and Enhanced Bat Algorithm for Parameter Optimization in Neural Networks

The staggering development of cyber threats has propelled experts, professionals and specialists in the field of security into the development of more dependable protection systems, including effective intrusion detection system (IDS) mechanisms which are equipped for boosting accurately detected threats and limiting erroneously detected threats simultaneously. Nonetheless, the proficiency of the IDS framework depends essentially on extracted features from network traffic and an effective classifier of the traffic into abnormal or normal traffic. The prime impetus of this study is to increase the performance of the IDS on networks by building a two-phase framework to reinforce and subsequently enhance detection rate and diminish the rate of false alarm. The initial stage utilizes the developed algorithm of a proficient wrapper-approach-based feature selection which is created on a multi-objective BAT algorithm (MOBBAT). The subsequent stage utilizes the features obtained from the initial stage to categorize the traffic based on the newly upgraded BAT algorithm (EBAT) for training multilayer perceptron (EBATMLP), to improve the IDS performance. The resulting methodology is known as the (MOB-EBATMLP). The efficiency of our proposition has been assessed by utilizing the mainstream benchmarked datasets: NLS-KDD, ISCX2012, UNSW-NB15, KDD CUP 1999, and CICIDS2017 which are established as standard datasets for evaluating IDS. The outcome of our experimental analysis demonstrates a noteworthy advancement in network IDS above other techniques.

Multi-Objective Parallel Task Offloading and Content Caching in D2D-aided MEC Networks

In device to device (D2D) aided mobile edge computing (MEC) networks, by implementing content caching and D2D links, the edge server and nearby mobile devices can provide task offloading platforms. For parallel tasks, proper decisions on content caching and task offloading help reduce delay and energy consumption. However, what is often ignored in the previous works is the joint optimization of parallel task offloading and content caching. In this paper, we aim to find optimal content caching and parallel task offloading strategies, so as to minimize task delay and energy consumption. The minimization problem is formulated as a multi-objective optimization problem, concerning both content caching and parallel task offloading. The content caching is formulated as an integer knapsack problem (IKP). To solve the IKP problem, an enhanced Binary Particle Swarm Optimization algorithm is proposed. The parallel task offloading problem is formulated as a constrained multi-objective optimization problem, an improved multi-objective bat algorithm is proposed to address the problem. Experimental results show that our algorithm can decrease delay and energy cost by at most 45&#x0025; and 56&#x0025;, respectively. In addition, the parallel task offloading ratio remains over 91&#x0025; even with large number of mobile devices (MDs).

A multi-objective bat algorithm with a novel competitive mechanism and its application in controller tuning

With the complexity of multi-objective optimization scenarios, both convergence and diversity of optimization algorithms are put forward higher requirements, but the harmony between the two has not been completely resolved, especially in controller tuning issues. To address this challenge, a novel competitive mechanism-based multi-objective bat algorithm is proposed in this paper. Firstly, based on a pairwise competition strategy, a competitive bat algorithm is designed as a candidate evolution strategy that promotes the population to converge quickly without cumbersome external archives. Furthermore, to avoid premature convergence, the genetic algorithm is utilized to diversity the swarm. Secondly, a designed tribal competition mechanism achieves a dynamical adjusting of evolution strategies according to maturity. Thence, the proposed algorithm can effectively balance the convergence and diversity through the adaptive complementation of multiple evolution strategies. Finally, experiments on benchmark functions illustrate that the proposed algorithm statistically outperforms the compared 12 state-of-the-art algorithms on at least 15 out of 19 instances. Besides, the proposed algorithm is used to solve multi-objective tuning problems of two widely used controllers in a laboratory-developed permanent magnet synchronous motor system. Comparison with 4 representative algorithms verifies its effectiveness and practicality in real-life multi-objective optimization problems.

A Hybrid Multi-Objective Bat Algorithm for Solving Cloud Computing Resource Scheduling Problems

To improve the service quality of cloud computing, and aiming at the characteristics of resource scheduling optimization problems, this paper proposes a hybrid multi-objective bat algorithm. To prevent the algorithm from falling into a local minimum, the bat population is classified. The back-propagation algorithm based on the mean square error and the conjugate gradient method is used to increase the loudness in the search direction and the pulse emission rate. In addition, the random walk based on lévy flight is also used to improve the optimal solution, thereby improving the algorithm’s global search capability. The simulation results prove that the multi-objective bat algorithm proposed in this paper is superior to the multi-objective ant colony optimization algorithm, genetic algorithm, particle swarm algorithm, and cuckoo search algorithm in terms of makespan, degree of imbalance, and throughput. The cost is also slightly better than the multi-objective ant colony optimization algorithm and the multi-objective genetic algorithm.

A multi-objective scheduling method for operational coordination time using improved triangular fuzzy number representation.

In modern warfare, the comprehensiveness of combat domain and the complexity of tasks pose great challenges to operational coordination.To address this challenge, we use the improved triangular fuzzy number to express the combat mission time, first present a new multi-objective operational cooperative time scheduling model that takes the fluctuation of combat coordinative time and the time flexibility between each task into account. The resulting model is essentially a large-scale multi-objective combinatorial optimization problem, intractably complicated to solve optimally. We next propose multi-objective improved Bat algorithm based on angle decomposition (MOIBA/AD) to quickly identify high-quality solutions to the model. Our proposed algorithm improves the decomposition strategy by replacing the planar space with the angle space, which helps greatly reduce the difficulty of processing evolutionary individuals and hence the time complexity of the multi-objective evolutionary algorithm based on decomposition (MOEA/D). Moreover, the population replacement strategy is enhanced utilizing the improved bat algorithm, which helps evolutionary individuals avoid getting trapped in local optima. Computational experiments on multi-objective operational cooperative time scheduling (MOOCTS) problems of different scales demonstrate the superiority of our proposed method over four state-of-the-art multi-objective evolutionary algorithms (MOEAs), including multi-objective bat Algorithm (MOBA), MOEA/D, non-dominated sorting genetic algorithm version II (NSGA-II) and multi-objective particle swarm optimization algorithm (MOPSO). Our proposed method performs better in terms of four performance criteria, producing solutions of higher quality while keeping a better distribution of the Pareto solution set.

Using Multi-Objective Bat Algorithm for Solving Multi-Objective Non-linear Programming Problem

Human beings are greatly inspired by nature. Nature has the ability to solve very complex problems in its own distinctive way. The problems around us are becoming more and more complex in the real time and at the same instance our mother nature is guiding us to solve these natural problems. Nature gives some of the logical and effective ways to find solutions to these problems. Nature acts as an optimized source for solving the complex problems. Decomposition is a basic strategy in traditional multi-objective optimization. However, it has not yet been widely used in multi-objective evolutionary optimization. &#x0D; Although computational strategies for taking care of Multi-objective Optimization Problems (MOPs) have been accessible for a long time, the ongoing utilization of Evolutionary Algorithm (EAs) to such issues gives a vehicle to tackle extremely enormous scope MOPs.&#x0D; MOBATD is a multi-objective bat algorithm that incorporates the dominance concept with the decomposition approach. Whilst decomposition simplifies the MOP by rewriting it as a set of Tchebycheff Approach, solving these problems simultaneously, within the BAT framework, might lead to premature convergence because of the leader selection process which uses the Tchebycheff Approach as a criterion. Dominance plays a major role in building the leaders archive, allowing the selected leaders to cover less dense regions while avoiding local optima and resulting in a more diverse approximated Pareto front. The results from 5 standard MOPs show that the MOBATD outperforms some developmental methods based on decomposition. All the results were achieved by MATLAB (R2017b).

Improved multiobjective bat algorithm for the credibilistic multiperiod mean-VaR portfolio optimization problem

Improved multiobjective bat algorithm for the credibilistic multiperiod mean-VaR portfolio optimization problem

Multi-Objective Optimization Method for Proper Configuration of Grid-Connected PV-Wind Hybrid System in Terms of Ecological Effects, Outlay, and Reliability

The assessment of the performance of grid hybrid frameworks depends primarily on the costs and reliability, associated with reduced greenhouse gas (GHG) emissions of the system. In this work, with objectives based on the minimization of two optimization features, namely loss of power supply probability (LPSP) and cost of energy (COE), multi-objective optimization of a grid-connected PV/wind turbine framework was implemented in the Faculty of Engineering in Gharyan, Libya, with the aim of providing adequate electricity, while optimizing the system’s renewable energy fraction (REF) was the third objective. This research also aimed to estimate the resulting amount of power produced by the hybrid system and mathematical models were submitted. The results showed the share of the total energy supplying the electricity demand for each part of the network. This study subsequently explored the interrelationship of the grid and the proposed hybrid system in relation to the capacity of the network to sell or obtain electricity from the hybrid system. In addition, multi-objective bat algorithm (MOBA) findings were divided into three dominant regions: the first region was the economically optimal solution (lowest COE), the second region was the conceptual perspective of utilizing renewable energies (highest REF), and the final region was the optimal solution with optimal environmental effects (lowest GHG emissions).

Optimisation of optical burst switched networks using multi-objective BAT algorithm

There is a growing demand for OBS (optical burst switching) networks, which is based on the concept of providing switching of data bursts without any intermediate switching and O/E/O conversions. The OBS technology is very economical and flexible to create new definitions for networking processes. OBS networks can also help in creating several types of new services which involve more bandwidth usage. The present research paper focuses on the OBS network and its benefits. The major problems of burst contention and congestion control using OBS networks are addressed in this research. In this paper, a possible solution to burst contention and congestion control is presented by the use of an algorithm, called BAT algorithm. The multi-objective optimisation problem is solved using a multi-objective BAT algorithm (MOBA). The proposed algorithm presented effective results to maximise the traffic demand, burst loss ratio minimisation, and reduction in overall burst block probability.

Optimisation of optical burst switched networks using multi-objective BAT algorithm

Optimisation of optical burst switched networks using multi-objective BAT algorithm

Application of nature inspired algorithms for multi-objective inventory control scenarios

An inventory control system having multiple items in stock is developed in this paper to optimize total cost of inventory and space requirement. Inventory modeling for both the raw material storage and work in process (WIP) is designed considering independent demand rate of items and no volume discount. To make the model environmentally aware, the equivalent carbon emission cost is also incorporated as a cost function in the formulation. The purpose of this study is to minimize the cost of inventories and minimize the storage space needed. The inventory models are shown here as a multi-objective programming problem with a few nonlinear constraints which has been solved by proposing a meta-heuristic algorithm called multi-objective particle swarm optimization (MOPSO). A further meta-heuristic algorithm called multi-objective bat algorithm (MOBA) is used to determine the efficacy of the result obtained from MOPSO. Taguchi method is followed to tune necessary response variables and compare both algorithm's output. At the end, several test problems are generated to evaluate the performances of both algorithms in terms of six performance metrics and analyze them statistically and graphically.

ECO-BAT: A New Routing Protocol for Energy Consumption Optimization Based on BAT Algorithm in WSN

Wireless sensor network (WSN) has been widely used due to its vast range of applications. The energy problem is one of the important problems influencing the complete application. Sensor nodes use very small batteries as a power source and replacing them is not an easy task. With this restriction, the sensor nodes must conserve their energy and extend the network lifetime as long as possible. Also, these limits motivate much of the research to suggest solutions in all layers of the protocol stack to save energy. So, energy management efficiency becomes a key requirement in WSN design. The efficiency of these networks is highly dependent on routing protocols directly affecting the network lifetime. Clustering is one of the most popular techniques preferred in routing operations. In this work we propose a novel energy-efficient protocol for WSN based on a bat algorithm called ECO-BAT (Energy Consumption Optimization with BAT algorithm for WSN) to prolong the network lifetime. We use an objective function that generates an optimal number of sensor clusters with cluster heads (CH) to minimize energy consumption. The performance of the proposed approach is compared with Low-Energy Adaptive Clustering Hierarchy (LEACH) and Energy Efficient cluster formation in wireless sensor networks based on the Multi-Objective Bat algorithm (EEMOB) protocols. The results obtained are interesting in terms of energy-saving and prolongation of the network lifetime.

Surrogate modeling-based multi-objective optimization for the integrated distillation processes

Although multi-objective optimization of integrated distillation processes can substantially improve process design, the nonlinearity and complexity of the process results in high computational expense for optimization. Here, an approach incorporating surrogate modeling into multi-objective optimization is proposed, in which surrogate models for function evaluation are constructed by using the RBF neural network. Central composite design was adopted as a sampling strategy and surrogate models were individually constructed for different optimization objectives to improve prediction accuracy. Multi-objective bat algorithm was set as an optimizer to obtain the Pareto front. This surrogate modeling-based multi-objective optimization approach was applied to the design of dividing wall column and side-reactor column configuration, and the satisfied design options realizing the trade-offs between capital and operating costs were successfully obtained thereafter.

Arrhythmia Classification with ECG signals based on the Optimization-Enabled Deep Convolutional Neural Network

Arrhythmia classification is the need of the hour as the world is reporting a higher death troll as a cause of cardiac diseases. Most of the existing methods developed for arrhythmia classification face a hectic challenge of classification accuracy and they raised the challenge of automatic monitoring and classification methods. Accordingly, the paper proposes the automatic arrhythmia classification strategy using the optimization-based deep convolutional neural network (deep CNN). The optimization algorithm named, Bat-Rider optimization algorithm (BaROA) is newly developed using the multi-objective bat algorithm (MOBA) and Rider Optimization Algorithm (ROA).At first, the wave and gabor features are extracted from the ECG signals in such a way that these features represent the individual ECG features. Finally, the signals are provided to the BaROA-based DCNN classifier that identifies conditions of the individual as arrhythmia and no-arrhythmia from the ECG signals. The methods are analyzed using the MIT-BIH Arrhythmia Database and the analysis is performed based on the evaluation parameters, like accuracy, specificity, and sensitivity, which are found to be 93.19 %, 95 %, and 93.98 %, respectively.