Mutable Smart Bee Research Articles

Fabrication of a resonant PEMC sensor using hybrid $$\upvarepsilon $$ ε -constraint lexicographical ranking DE-MSBA with Jacobean approximation

In the current research, a novel algorithm is proposed for the optimal fabrication of a resonant piezoelectric excited millimeter-sized cantilever (PEMC) sensor. PEMC sensor is one of the most well-known types of sensors with a wide range of applications in today’s industry. The proposed design mechanism is general, and can be used for the fabrication of PEMCs for different applications. A physics-based analytical model based on Euler–Bernoulli theory is adopted for performance analysis. A novel hybrid nature-inspired optimizer, called $$\upvarepsilon $$ -constraint lexicographical ranking differential evolution mutable smart bee algorithm ( $$\upvarepsilon $$ lr-DEMSBA), is developed which can effectively search the non-convex and multimodal solution domain. The optimal values of PEMC design parameters are obtained using $$\upvarepsilon $$ lr-DEMSBA. To ensure the veracity of the parameters suggested by $$\upvarepsilon $$ lr-DEMSBA, a PEMC is fabricated using the suggested optimal values, and some post analysis are carried out. By investigating the relation between the frequency shift of natural frequency and increasing of immersion depth, it is observed that the linear performance zone of optimized PEMC sensor is increased by 2.5 mm which is a favorable property. Also, from numerical viewpoint, it was observed that $$\upvarepsilon $$ lr-DEMSBA surpasses different variants of optimization techniques for the current case study.

Vector mutable smart bee algorithm for engineering optimisation

Optimising and controlling complex engineering systems is a phenomenon that attracts the interest of numerous scientists. Till now, a variety of intelligent optimising and controlling techniques such as neural networks, fuzzy logic, game theory, support vector machines and stochastic algorithms were proposed to facilitate the engineering systems controlling process. Recently, a new optimising method called mutable smart bee (MSB) algorithm was proposed for optimising complex multi-modal problems. It has been shown that the method is a fast, powerful and robust optimising method since it hires a finite number of smart investigating agents in the problem's solution space. Here, a new concept of this model is inspired for preparing MSB algorithm to be applied on a multi-objective problem. Besides, some well-known Pareto base optimising algorithms such as non-dominated sorting genetic algorithm (NSGA-II) and strength Pareto evolutionary algorithm (SPEA 2) are utilised to confirm the acceptable performance of proposed method.

Heat transfer analysis of unsteady graphene oxide nanofluid flow using a fuzzy identifier evolved by genetically encoded mutable smart bee algorithm

In the current research, the unsteady two dimensional Graphene Oxide water based nanofluid heat transfer between two moving parallel plates is analyzed using an intelligent black-box identifier. The developed intelligent tool is known as evolvable evolutionary fuzzy inference system (EE-FIS) which is based on the integration of low-level fuzzy programming and hyper-level evolutionary computing concepts. Here, the authors propose the use of a modified evolutionary algorithm (EA) which is called hybrid genetic mutable smart bee algorithm (HGMSBA). The proposed HGMSBA is used to evolve both antecedent and consequent parts of fuzzy rule base. Besides, it tries to prune the rule base of fuzzy inference system (FIS) to decrease its computational complexity and increase its interpretability. By considering the prediction error of the fuzzy identifier as the objective function of HGMSBA, an automatic soft interpolation machine is developed which can intuitively increase the robustness and accuracy of the final model. Here, HGMSBA-FIS is used to provide a nonlinear map between inputs, i.e. nanoparticles solid volume fraction (ϕ), Eckert number (Ec) and a moving parameter which describes the movements of plates (S), and output, i.e. Nusselt number (Nu). Prior to proceeding with the modeling process, a comprehensive numerical comparative study is performed to investigate the potentials of the proposed model for nonlinear system identification. After demonstrating the efficacy of HGMSBA for training the FIS, the system is applied to the considered problem. Based on the obtained results, it can be inferred that the developed HGMSBA-FIS black-box identifier can be used as a very authentic tool with respect to accuracy and robustness. Besides, as the proposed black-box is not a physics-based identifier, it frees experts from the cumbersome mathematical formulations, and can be used for advanced real-time applications such as model-based control. The simulations indicate that the gradient of Nu has a direct nonlinear relation with the values of ϕ and Ec. It is also observed that an increase in the value of S decreases the value of Nu.

Coupling Gaussian generalised regression neural network and mutable smart bee algorithm to analyse the characteristics of automotive engine coldstart hydrocarbon emission

In this paper, the authors intend to attest the applicability of computational intelligence for tackling a demanding real-life engineering problem, that is analysing the amounts of exhaust gas temperature () and the engine-out hydrocarbon emission () during the coldstart operation of an automotive engine with respect to the variation of engine speed (), spark timing (Δ) and air/fuel ratio. It has been proven that the coldstart phenomenon is highly transient, nonlinear and uncertain and therefore has absorbed an increasing attention of the researchers of automotive society. In this paper, we prove that a proper integration of intelligent methods can result in a very efficient tool suited for identifying the characteristics of coldstart phenomenon. To do so, a recent spotlighted natural-inspired optimiser called mutable smart bee algorithm is utilised to evolve a Gaussian generalised regression neural network such that the resulted framework can conduct a fast, robust and accurate identification. The outcomes of the conducted experiments endorse on the applicability of intelligent techniques for engine coldstart identification, and also, pave the way for future investigations on intelligent-based analysis of demanding automotive problems.

The Great Salmon Run Metaheuristic for Robust Shape and Size Design of Truss Structures with Dynamic Constraints

In this investigation, the authors intend to demonstrate the applicability of a recent spotlighted metaheuristic called the great salmon run (TGSR) algorithm for shape and size design of truss structures. The algorithmic functioning of TGSR emulates the annual migration of salmons together with dangers laid through their pathways. In a previous study by the authors, it has been proved that the method is as effective as most of the state-of-the-art metaheuristics for a wide range of numerical benchmark problems. Here, the authors utilize TGSR together with some rival metaheuristics, i.e. bee algorithm (BA), scale factor local search differential evolutionary algorithm (SFLSDEA), chaotic particle swarm optimization (CPSO) algorithm, self adaptive penalty function genetic algorithm (SAPFGA) and mutable smart bee algorithm (MSBA), for optimal design of truss structures with dynamic frequency constraints. To effectively handle the constraints, the authors take the advantage of self-adaptive penalty function (SAPF) constraint handling technique to free the user from any priori penalty coefficient tuning. Therefore, an algorithm for automation of constraint shape and size design of truss structures is proposed here. Furthermore, for more elaboration, the authors consider the results of some previous reports for same problems to find out whether TGSR is capable of yielding comparative results as compared to other metaheuristics. Through the experiments, the exploration/exploitation capabilities of TGSR for truss design are investigated. It is proved that TGSR is not only able to handle the nonlinearities and decision making difficulties associated with shape and size optimization of truss structures but also can show comparative results as compared to powerful state-of-the-art metaheuristics.

Intelligent design of waste heat recovery systems using thermoelectric generators and optimization tools

Optimal design of thermal systems that effectively use energy resources is one of the foremost challenges that researchers almost confront. Until now, several researches have been made to enhance the performance of major thermal systems. In this investigation, the authors try to make a conceptual design to maximize the electricity demand of Damavand power plant as the biggest thermal system in Middle East sited in Iran. The idea of designing is laid behind applying a number of thermoelectric modules within the condenser in order to recover the waste heat of the thermal systems. Besides, the authors have developed some intelligent tools to elaborate on the performance of their proposed model. Firstly, an artificial neural network has been utilized to estimate the potential power generation of the thermoelectric modules. At the second step, computational fluid dynamic solver, FLUENT is used to determine the variation of the temperature through the length of the thermoelectric module assembly. Based on the gained results, an intelligent multi-objective optimization algorithm called Pareto based mutable smart bee is developed to optimize the properties of the thermoelectric component.

Ensemble mutable smart bee algorithm and a robust neural identifier for optimal design of a large scale power system

The aim of the current study is to probe the potentials of ensemble bio-inspired approaches to handle the deficiencies associated with designing large scale power systems. Ensemble computing has been proven to be a very promising paradigm. The fundamental motivation behind designing such bio-inspired optimization models lies in the fact that interactions among different sole optimizers can afford much better income as compared with an individual optimizer. To do so, the authors propose an optimization technique called ensemble mutable smart bee algorithm (E-MSBA) which is based on the aggregation of several independent low-level optimizers. Here, each low-level unit of the proposed ensemble framework uses mutable smart bee algorithm (MSBA) for optimization procedure. The main provocations behind selecting MSBAs of different properties as components of ensemble are twofold. On the one hand, MSBA proved its capability for handling multimodal constraint problems. On the other hand, based on different experiments, it was demonstrated that MSBA can find the optimum solution with a relatively low computational cost. In this study, the authors intend to indicate that the proposed ensemble paradigm can efficiently optimize the operating parameters of a large scale power system which includes different mechanical components. To this end, E-MSBA and some rival methods are taken into account for the optimization procedure. The obtained results reveal that E-MSBA inherits some positive features of the MSBA algorithm. Additionally, it is observed that the ensembling approach enables the proposed method to effectively tackle the flaws associated with optimization of large scale problems.

Optimal Arrangement of Thermoelectric Modules for Recovering the Waste Heat of Damavand Power Plant using Mutable Smart Bee and Neuro-Fuzzy System

In current research, the authors intend to find the optimal arrangement of planar thermoelectric modules to maximize the generated electricity and minimize the maintenance cost of a power system called Damavand power plant. Optimal arrangement is done by mounting a number of thermoelectric modules within the condenser in order to recover the waste heat of the thermal system. For this purpose, firstly, an adaptive neuro fuzzy inference system (ANFIS) is utilized to model the generated power of thermoelectric module and the corresponding maintenance cost. Thereafter, Gambit and Fluent softwares are applied to evaluate the temperature rate through the length of thermoelectric component. Given the derived information, the authors use a recent spotlighted metaheuristic called mutable smart bee algorithm (MSBA) to optimally arrange the thermoelectric modulus for recovering the waste heat of Damavand power plant. Based on the obtained numerical results, it is proven that the proposed frame work is completely capable of finding a practical arrangement of thermoelectric cells within the entry of condenser component.

Optimal design of classic Atkinson engine with dynamic specific heat using adaptive neuro-fuzzy inference system and mutable smart bee algorithm

In this article, an improved version of Artificial Bee Colony (ABC) algorithm is developed to optimize a multi-modal thermodynamic power system with dynamic specific heat. Since original Karaboga's ABC for constraint problems does not consider the initial population to be feasible, a modified method called “Mutable Smart Bee Algorithm” (MSBA) is used which utilizes mutable heuristic agents. These mutable agents are also capable to maintain their historical memory for the location and quality of food sources. These features have been found as strong elements for mining data in constraint areas. In additions, our implementations reveal that MSBA is faster than Karaboga's method. To elaborate on authenticity of MSBA, several state-of-the-art techniques are used as rival methods to optimize well-known benchmark problems. Then, two main steps are made to optimize Atkinson engine. Firstly, an Adaptive Neuro-Fuzzy Inference System (ANFIS) is developed to identify the dynamic behavior of specific heat. Then, MSBA is hired to design the optimum features of the engine. It is observed that the proposed method is capable to successfully handle the real-life engineering problem as well as the numerical benchmark problems.

Vector optimization of laser solid freeform fabrication system using a hierarchical mutable smart bee-fuzzy inference system and hybrid NSGA-II/self-organizing map

The purpose of current investigation is to develop a robust intelligent framework to achieve efficient and reliable operating process parameters for laser solid freeform fabrication (LSFF) process as a recent and ongoing topic of investigation. Firstly, based on mutable smart bee algorithm (MSBA) and fuzzy inference system (FIS) two models are developed to identify the clad hight (deposited layer thickness) and the melt pool depth as functions of scanning speed, laser power and mass powder. Using the obtained model, the well-known multiobjective evolutionary algorithm called non-dominated sorting genetic algorithm (NSGA-II) is used for multi-criterion optimization of LSFF process. According to the available reported information and also the author's experiments, it is observed that the obtained Pareto front is not justifiable since it fails to cover the entire Pareto hyper-volume due to the lack of intensified exploration. To tackle this deficiency, authors execute a post optimization process through utilizing a competitive unsupervised machine learning approach known as self-organizing map (SOM) with cubic spatial topology. Achieved results indicate that this grid based network is capable of enhancing the intensification of Pareto solutions since its synaptic weights successfully imitate the characteristics of non-dominated solutions (optimal values of mass powder, laser power and scanning speed). For extracting the corresponding objective functions of these non-dominated synaptic weights, MSBA---FIS is used again to map the operating parameters to objective functions space. After the termination of abovementioned procedures, a valuable archive, containing a set of non-dominated solutions, is obtained which lets the authors to make a deliberate engineering trade-off. Simulation experiments reveal that the proposed intelligent framework is highly capable to cope with complex engineering systems. Besides, it is observed that MSBA is more efficient in evolving the structure of hierarchical fuzzy inference system in comparison with classic hierarchical GA-FIS model. This rises from the simple structure of MSBA that turns it into a fast and robust algorithm for handling constraint distributed systems (i.e. hierarchical FIS in current investigation). The obtained results also indicate that the introduced intelligent framework is applicable for optimal design of complex engineering systems where there exists no analytical formulation that describes the phenomenon as well as information of optimal operating parameters.

Comprehensive preference optimization of an irreversible thermal engine using pareto based mutable smart bee algorithm and generalized regression neural network

Optimizing and controlling of complex engineering systems is a phenomenon that has attracted an incremental interest of numerous scientists. Until now, a variety of intelligent optimizing and controlling techniques such as neural networks, fuzzy logic, game theory, support vector machines and stochastic algorithms were proposed to facilitate controlling of the engineering systems. In this study, an extended version of mutable smart bee algorithm (MSBA) called Pareto based mutable smart bee (PBMSB) is inspired to cope with multi-objective problems. Besides, a set of benchmark problems and four well-known Pareto based optimizing algorithms i.e. multi-objective bee algorithm (MOBA), multi-objective particle swarm optimization (MOPSO) algorithm, non-dominated sorting genetic algorithm (NSGA-II), and strength Pareto evolutionary algorithm (SPEA 2) are utilized to confirm the acceptable performance of the proposed method. In order to find the maximum exploration potentials, these techniques are equipped with an external archive. These archives aid the methods to record all of the non-dominated solutions. Eventually, the proposed method and generalized regression neural network (GRNN) are simultaneously used to optimize the major parameters of an irreversible thermal engine. In order to direct the PBMSB to explore deliberate spaces within the solution domain, a reference point obtained from finite time thermodynamic (FTT) approach, is utilized in the optimization. The outcome results show the acceptable performance of the proposed method to optimize complex real-life engineering systems.

Optimising maximum power output and minimum entropy generation of Atkinson cycle using mutable smart bees algorithm

The purpose of this article is optimising maximum power output (MPO) and minimum entropy generation (MEG) of an Atkinson cycle as a multi-objective constraint thermodynamic problem by a new improved artificial bee colony algorithm which utilises 'mutable smart bee' (MSB) instead of conventional bees. The results have been checked with some of the most common optimising algorithms like Karaboga's original artificial bee colony, bees algorithm (BA), improved particle swarm optimisation (IPSO), Lukasik firefly algorithm (LFFA) and self-adaptive penalty function genetic algorithm (SAPF-GA). Mutable smart bee (MSB) is able to maintain its historical memories for the location and quality of food sources and also a little chance of mutation during the searching process is considered for this bee. These features were found as strong elements for mining data in constraint areas and the results will prove this claim.

Multiobjective Optimization of Irreversible Thermal Engine Using Mutable Smart Bee Algorithm

A new method called mutable smart bee (MSB) algorithm proposed for cooperative optimizing of the maximum power output (MPO) and minimum entropy generation (MEG) of an Atkinson cycle as a multiobjective, multi-modal mechanical problem. This method utilizes mutable smart bee instead of classical bees. The results have been checked with some of the most common optimizing algorithms like Karaboga’s original artificial bee colony, bees algorithm (BA), improved particle swarm optimization (IPSO), Lukasik firefly algorithm (LFFA), and self-adaptive penalty function genetic algorithm (SAPF-GA). According to obtained results, it can be concluded that Mutable Smart Bee (MSB) is capable to maintain its historical memory for the location and quality of food sources and also a little chance of mutation is considered for this bee. These features were found as strong elements for mining data in constraint areas and the results will prove this claim.

Optimal design of constraint engineering systems: application of mutable smart bee algorithm

In the current investigation, a new optimisation technique called mutable smart bee algorithm (MSBA) is used for optimal design of real-life engineering systems that are subjected to different types of constraints. MSBA is a memory-based diversified optimisation technique that hires mutable smart bee (MSB) instead of conventional bee. MSB heuristic agents are capable of maintaining their historical memory for the location and quality of food sources and also a little chance of mutation is considered for them. Exerted experiments reveal that these features are really effective for optimising multi-modal constraint problems. To elaborate on the authenticity of MSBA, obtained results are compared to state-of-the-art optimisation techniques.