Nature Based Optimization Algorithms Research Articles

Multi-population Black Hole Algorithm for the problem of data clustering.

The retrieval of important information from a dataset requires applying a special data mining technique known as data clustering (DC). DC classifies similar objects into a groups of similar characteristics. Clustering involves grouping the data around k-cluster centres that typically are selected randomly. Recently, the issues behind DC have called for a search for an alternative solution. Recently, a nature-based optimization algorithm named Black Hole Algorithm (BHA) was developed to address the several well-known optimization problems. The BHA is a metaheuristic (population-based) that mimics the event around the natural phenomena of black holes, whereby an individual star represents the potential solutions revolving around the solution space. The original BHA algorithm showed better performance compared to other algorithms when applied to a benchmark dataset, despite its poor exploration capability. Hence, this paper presents a multi-population version of BHA as a generalization of the BHA called MBHA wherein the performance of the algorithm is not dependent on the best-found solution but a set of generated best solutions. The method formulated was subjected to testing using a set of nine widespread and popular benchmark test functions. The ensuing experimental outcomes indicated the highly precise results generated by the method compared to BHA and comparable algorithms in the study, as well as excellent robustness. Furthermore, the proposed MBHA achieved a high rate of convergence on six real datasets (collected from the UCL machine learning lab), making it suitable for DC problems. Lastly, the evaluations conclusively indicated the appropriateness of the proposed algorithm to resolve DC issues.

Metaheuristic nature-inspired algorithms for reservoir optimization operation: a systematic literature review

<span>The purpose of this systematic literature review (SLR) article is to discuss the findings of the state-of-art metaheuristic nature-inspired algorithm (MHNIA) in reservoir optimization operation. The rationale of this approach is to elucidate the optimal way as decision making that implemented MHNIA for several complex problems in reservoir optimization operation. Commonly, the metaheuristic optimization algorithm has always been used in hydrology field, especially in reservoir optimization. Hence, this presented study reviewed a considerable amount from the previous studies of commonly nature-based optimization algorithms applied in reservoir operations. Hence, preferred reporting items for systematic review and meta-analyses (PRISMA) has been used as guidance. The source was utilized from two primary journal databases: Scopus and web of science. According to the proposed search string, the findings managed to express into nine main themes which are optimize in water release, optimize reservoir operation problems, optimize hydropower operation, optimize condensate fluids in reservoir storage, optimize water pumped storage, optimize water quality control, optimize system performance operation, optimize water demand and optimize reservoir control as flood preventing. Overall, 24 articles that passed the minimum quality were retrieved using systematic searching strategies.</span>

Snow Leopard Optimization Algorithm: A New Nature-Based Optimization Algorithm for Solving Optimization Problems

Numerous optimization problems have been defined in different disciplines of science that must be optimized using effective techniques. Optimization algorithms are an effective and widely used method of solving optimization problems that are able to provide suitable solutions for optimization problems. In this paper, a new nature-based optimization algorithm called Snow Leopard Optimization Algorithm (SLOA) is designed that mimics the natural behaviors of snow leopards. SLOA is simulated in four phases including travel routes, hunting, reproduction, and mortality. The different phases of the proposed algorithm are described and then the mathematical modeling of the SLOA is presented in order to implement it on different optimization problems. A standard set of objective functions, including twenty-three functions, is used to evaluate the ability of the proposed algorithm to optimize and provide appropriate solutions for optimization problems. Also, the optimization results obtained from the proposed SLOA are compared with eight other well-known optimization algorithms. The optimization results show that the proposed SLOA has a high ability to solve various optimization problems. Also, the analysis and comparison of the optimization results obtained from the SLOA with the other eight algorithms shows that the SLOA is able to provide more appropriate quasi-optimal solutions and closer to the global optimal, and with better performance, it is much more competitive than similar algorithms.

Optimization Algorithms: Optimal Parameters Computation for Modeling the Polarization Curves of a PEFC Considering the Effect of the Relative Humidity

The development of green energy conversion devices has been promising to face climate change and global warming challenges over the last few years. Energy applications require a confident performance prediction, especially in polymer electrolyte fuel cell (PEFC), to guarantee optimal operation. Several researchers have employed optimization algorithms (OAs) to identify operating parameters to improve the PEFC performance. In the current study, several nature-based OAs have been performed to compute the optimal parameters used to describe the polarization curves in a PEFC. Different relative humidity (RH) values, one of the most influential variables on PEFC performance, have been considered. To develop this study, experimental data have been collected from a lab-scale fuel cell test system establishing different RH percentages, from 18 to 100%. OAs like neural network algorithm (NNA), improved grey-wolf optimizer (I-GWO), ant lion optimizer (ALO), bird swarm algorithm (BSA), and multi-verse optimization (MVO) were evaluated and compared using statistical parameters as training error and time. Results gave enough information to conclude that NNA had better performance and showed better results over other highlighted OAs. Finally, it was found that sparsity and noise are more present at lower relative humidity values. At low RH, a PEFC operates under critical conditions, affecting the fitting on OAs.

A Hybrid Black Hole Algorithm with Genetic Algorithm for Solving Data Clustering Problems

Clustering is a process of randomly selecting k-cluster centers also grouping the data around those centers. Issues of data clustering have recently received research attention and as such, a nature-based optimization algorithm called Black Hole (BH) has said to be suggested as an arrangement to data clustering issues. The BH as a metaheuristic which is elicited from public duplicates the black hole event in the universe, whereas circling arrangement in the hunt space addresses a solo star. Even though primordial BH has shown enhanced execution taking place standard datasets, it doesn't have investigation capacities yet plays out a fine local search. In this paper, another crossover metaheuristic reliant on the mix of BH algorithm as well as genetic algorithm suggested. Genetic algorithm represents its first part of the algorithm which prospects the search space and provides the initial positions for the stars. Then, the BH algorithm utilizes the search space and finds the best solution until the termination condition is reached. The proposed hybrid approach was estimated on synchronized nine popular standard functions where the outcomes indicated that the process generated enhanced outcome with regard to robustness compared to BH and the benchmarking algorithms in the study. Furthermore, it also revealed a high convergence rate which used six real datasets sourced of the UCI machine learning laboratory, indicating fine conduct of the hybrid algorithm on data clustering problems. Conclusively, the investigation showed the suitability of the suggested hybrid algorithm designed for resolving data clustering issues.

The Concentric Elliptical Antenna Array Patterns Synthesis Using Marine Predators Algorithm

In this paper, the pattern synthesis of concentric elliptical antenna arrays (CEAAs) for effective antenna design is presented. The current amplitudes and angular positions values of the CEAA elements are optimally found by using the Marine Predators Algorithm (MPA) in order to obtain the optimum radiation pattern. The MPA method is a novel nature-based optimization algorithm that is inspired by ocean predators foraging strategy. The antenna arrays with reduced maximum side lobe levels (MSL) and narrowed half power beam widths (HPBW) are an efficient antenna with optimum radiation pattern to suppress interference and increase directionality. In addition, CEAAs with different numbers of elements are examined to show the flexibility and performance of the proposed algorithm in this study. For 18-element CEAA, MPA obtained the HPBW between 29.18° and 31.34°, and MSL between − 20.70 and − 25.89 dB. For 30-element CEAA, the optimum values of HPBW are between 20.13° and 21.38°, and the optimum values of MSL are between − 18.90 and − 20.50 dB. The results of pattern synthesis obtained by MPA are compared with other metaheuristic optimization methods, and it is seen that the results of the proposed method are generally better.

Metaheuristic pansharpening based on symbiotic organisms search optimization

This study proposed a metaheuristic pansharpening (MP) method, which performs in a Synthetic Variable Ratio (SVR)-like manner. The proposed method introduced the Symbiotic Organisms Search (SOS) algorithm, an advanced nature-based optimization algorithm, to estimate a weight for each multispectral (MS) band to achieve the optimum intensity. The SVR pansharpening formula was used as the objective function and the Root Mean Square Error (RMSE) metric was used as the fitness function of the SOS algorithm to optimize the intensity. The spectral and spatial quality of the results of the MP method were qualitatively and quantitatively compared against those of 15 widely-used pansharpening methods in 5 test sites in Turkey with different land cover features. The experiments aimed to spatially enhance WorldView-2 MS images by using WorldView-2 panchromatic (PAN) bands and a UAV-derived PAN orthophoto. It was also aimed to sharpen IKONOS MS images by using a QuickBird pansharpened image and an IKONOS PAN band. The MATLAB software was used to implement the proposed method and to compute the spatial and spectral quality metrics. The spatial quality of each pansharpened image was evaluated at full-scale, whereas the spectral quality of each pansharpened image was evaluated at both full-scale and reduced-scale. A scoring strategy based on giving a performance score with respect to the spatial and spectral quality metrics was used to ensure a fair comparison among the pansharpening methods used. The results demonstrated that, out of 16 pansharpening methods, the MP method achieved the highest overall spectral and spatial quality scores of 15.5 and 15.5, respectively. The proposed method was found to perform successfully with both singlesensor and multisensor input images. It was also concluded that the proposed method is able to deal with high spatial resolution ratio between the input images.

Productivity yielding in shell and tube heat exchanger by MCDM-NBO approach

The productivity of shell and tube heat exchangers are governed by various geometrical parameters like tube diameter, tube thickness, tube length, tube pitch, tube layout, installation area and baffle spacing of the heat exchanger. The operational efficiency of heat exchangers is highly influenced by the characteristics of heat exchanger parameters. The exchanger efficiency gets trapped due to many incongruities’ effects like over-pressure, bio-fouling, chemical fouling and corrosion. The selection of optimum design configuration is essential to achieve higher operational efficiency for a heat exchanger. But the performance and reliability of the optimization process play a key role in selecting and deselecting significant and insignificant parameters, respectively. So, cognitive selection of parameters and henceforth a reliable optimization process is required to identify optimal design for a heat exchanger. Moreover, economic factors also contribute to attain a consolidated yield result for a heat exchanger. This research proposes an optimal configuration with the help of ensemble output obtained by multi-criteria decision making and nature-based optimization algorithm. It has been found that exchange efficiency in optimal configuration is boosted by 22% from prototype heat exchanger.

ESTIMATION OF INDUCTION MOTOR EFFICIENCY VALUE BY NATURE BASED OPTIMIZATION ALGORITHM

Abstract. Three phase induction motors are most used and popular machines in industry. The maintenance and repairmen of these machines are easy. They have better performance in abnormal conditions rather than other machines such as direct drive machines or synchronous machines. Also there are more techniques to control the speed and position of these motors. In modern world, the cost of energy is high and we must remove high consumer devices. As mentioned, the high percentage of machines in industry is three phase induction motors. In this paperwe proposed an intelligent technique to estimate of three phase induction motors. This technique can help to determine non efficiency motors and replace them with better one. The proposed method uses some measurable parameter of three phase induction motors such as current of stator, the value of fed power, the magnitude of input voltage and the value of resistance in stator section to estimate the motor electrical circuit. For this purpose we used some nature based optimization algorithms such genetic algorithm (GA), cuckoo optimization algorithm (COA) and guided artificial bee colony (GABC) algorithm. These optimization algorithms have excellent features in exploration and extraction of global answer in search space. The proposed method is tested on real case and the obtained results show that the proposed method has good performance in three phase induction motor’s electrical circuit estimation. Also obtained results demonstrate that the GABC algorithm has better accuracy rather than other nature based optimization algorithms.

Electromagnetic Optimization Using Mixed-Parameter and Multiobjective Covariance Matrix Adaptation Evolution Strategy

Different variations of the covariance matrix adaptation evolution strategy (CMA-ES) are used in the design and optimization of electromagnetic (EM) problems. Two different schemes for the implementation of mixed-parameter CMA-ES and one scheme for the implementation of multiobjective CMA-ES are presented. Mixed-parameter CMA-ES is attractive in EM optimization when both continuous and discrete design parameters are involved. The first mixed-parameter scheme uses a Poisson mutation operator to update the discrete variables, and the second one forces an integer mutation on discrete variables with small variances. Multiobjective CMA-ES, developed in this paper, optimizes designs with respect to multiple objective functions simultaneously. It ranks the candidate solutions according to two levels: nondominated sorting and crowding distance. Several antenna and microwave design problems are presented to evaluate the performance of these schemes and compare them with other nature-based optimization algorithms.

Improvement of voltage profile and reactive power using genetic algorithm

Abstract. Electrical power network is the biggest hand make of human and this network growth every day. The electrical power network has many sections and variables. In this network, the voltage profile is the most important issue to be considered. The voltage profile is hardly associated with management of reactive power. For this purpose, this paper proposes an intelligent system to improve the voltage profile using reactive power management. The proposed system uses genetic algorithm to select the best parameters of power network. In the proposed method, we consider some important variables of electrical power network such as compensator location, compensators value, on line tap changeable transformers tap ratio. Also the proposed optimization algorithm is compared with other nature based optimization algorithms such as particle swarm optimization algorithm and imperialist competitive algorithm. The proposed system is tested on twenty five buses system and the obtained results show that the proposed system has good performance.

CUCKOO OPTIMIZATION ALGORITHM BASED DESIGN FOR LOW-SPEED LINEAR INDUCTION MOTOR

Abstract. In these years, linear induction machines (LIMs) are widely used in rapid transportation applications and these machines achieve thrust directly without gear device, link or axial mechanism system. Furthermore LIMs have numerous other benefits such as not complex body and easy repairing. Unfortunately LMIs have big disadvantage: not good efficiency and weak power factor. These disadvantages cause high energy loss and a rise in input current value, and occupy transmission line capacity. In this research article, a multiobjective optimization system based on cuckoo optimization algorithm (COA) to enhance both efficiency and power factor is introduced, concurrently. The suggested intelligent system is established on capability of nature-based optimization algorithms in selecting the optimal solution. One benchmark and standard example is applied to show the operation of the design method and optimization system. The advantage features of the COA is due to its capability to concurrently refine a local search, as searching globally solution in search space. Furthermore, computer simulation results demonstrate that proposed method have a low dependency on oscillation of the variables. Also, the applied optimization technique was very speedy, requiring a low time to discover the optimum result in search space.