Well-known Meta-heuristic Techniques Research Articles

Hybrid of Meta-Heuristic Techniques Based on Cuckoo Search and Particle Swarm Optimizations for Solar PV Systems Subjected to Partially Shaded Conditions

Solar energy has a significant role in meeting rising energy demand while reducing environmental impact. Solar radiation and temperature are important factors on which PV energy production depends, but its optimal operation point is influenced by variations in the aforementioned environmental factors. The nonlinear behavior of the solar system and the variable nature of environmental conditions make determining the optimal operation point difficult. To overcome these difficulties, maximum power point tracking (MPPT) finding techniques are used to extract the optimal power from the photovoltaic energy system. The behavior of MPPT varies for different weather conditions, such as partial shading conditions (PSC), and uniform irradiance conditions. Conventional techniques are simple, quick, and efficient for tracing the MPP quickly, but they are limited to uniform weather conditions. In addition, these techniques don't achieve the Global Maxima (GM) and mostly stay stuck at the Local Maxima (LM). The Meta-Heuristic techniques aid in finding the GM, but their primary disadvantage is that they take a longer time to trace the Global Maxima. This study addresses the problem by combining Cuckoo Search (CS) and Particle Swarm Optimization (PSO) algorithms, leading to a hybrid (CSPSO) technique to extract the global maximum (GM). To verify the effectiveness of the suggested technique, its performance is examined under three different irradiance patterns for different PV array configurations (such as 3S and 4S3P) through MATLAB simulation. The outcomes of CSPSO are compared with the prior well-known Meta-Heuristic techniques such as Cuckoo Search (CS), Particle Swarm Optimization (PSO), and Crow Search Algorithm (CSA). The results show the suggested technique excels over other techniques in terms of accuracy, tracking efficiency, and tracking speed. The suggested technique is capable of tracking GMPP with an average efficiency of 99.925% and an average tracking time of 0.13 s in all shading patterns studied.

Improved genetic algorithm for mobile robot path planning in static environments

The genetic algorithm (GA) is a well-known meta-heuristic technique for addressing the static mobile robot global path planning (MRGPP) issue. Current GA, however, has certain shortcomings, such as inefficient population initialization and low-quality solutions. As an enhanced GA, a Linear Rank-based, or Clearance-based Probabilistic Road Map (CBPRM), technique is proffered to overcome these difficulties. The new model guides the population initialization process by using the fitness score of each cell in the environment, lowering the number of infeasible pathways created. Furthermore, a genetic operator combination is proposed to balance the global and local search and increase the quality of the optimum path created in terms of path length and safety. Two experiments were carried out to assess the suggested GA. The novel population initialization strategy was compared to two current models in the first experiment, and the findings revealed that the suggested approach greatly decreases the number of infeasible pathways created and the time required for the process. The ideal genetic operator combination was determined in the second experiment, and the results revealed that the suggested combination improves the quality of the optimal path created in fewer iterations. In summary, the proposed GA improves on previous models by proposing a novel population initialization method and combining numerous genetic operators. These alterations improve the quality of the optimum path and indicate the suggested model’s potential for solving the MRGPP challenge.

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) and F-Score (90–100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC’2022 benchmarks functions.

An improved gorilla troops optimizer for global optimization problems and feature selection

The Artificial Gorilla Groups Optimizer (GTO) is a novel metaheuristic algorithm that takes its cues from the collective intelligence of wild gorilla troops. Although it has shown promise in solving various real-world applications, it may still get stuck at local optima and premature convergence when dealing with more complex optimization tasks. Because of these shortcomings, this research proposes a new modified Gorilla Groups Optimizer (mGTO) method, employing a set of operators to achieve a more stable balance between exploitation and exploration. These operators are the elite Opposition Based-Learning (EOBL), Cauchy Inverse Cumulative (CICD) Distribution Operator, and tangent Flight (TFO). Each operator is used to achieve a specific task during the search process. The EOBL aims to enhance the diversity of the population, which leads to discovering the feasible region. Whereas the integration between CICD and TFO is used to improve the population’s exploitation ability, which leads to an increase in the convergence rate. To validate the efficiency of the presented method, called mGTO, a set of experimental series is conducted using the CEC2020 benchmark and constraint design engineering problems. In addition, its applicability is assessed by implementing mGTO as a feature selection technique and applying it to improve the classification accuracy of sixteen datasets. The results of mGTO are also compared with those produced by other well-known meta-heuristic techniques. The statistical validity of the performance is also verified using Wilcoxon’s rank-sum test. The experimental results and comparison analysis reveal the consistent and better performance of the proposed mGTO method to solve optimization problems.

Global maximum power point tracking and cell parameter extraction in Photovoltaic systems using improved firefly algorithm

Optimization of power in Photovoltaic (PV) systems and extraction of cell parameters in PV cells using well-known metaheuristic techniques have been implemented by different researchers. However, under fast-changing irradiances and complex partial shading conditions, these algorithms underperform by getting trapped at the local maxima and with a poor convergence exploration, which in turn leads to a loss in energy supplied from a PV system. This paper proposes an innovative use of improved firefly algorithm (IFA) technique suitable for effective extraction of both the unknown cell parameters in PV cells and locating the global maximum power point (GMPP) that yields optimum power in PV array systems within the shortest possible time and with minimal loss in power when operated under different environmental conditions. To validate the effectiveness of the proposed IFA technique, six experimental analyses were conducted using four optimization techniques comprising of particle swarm optimization, cuckoo search algorithm, flower pollination algorithm, and the proposed IFA technique as case studies. Results achieved confirmed that IFA technique can be used to solve real-world problems in PV systems.

Energy harvesting and stability analysis of centralized TEG system under non-uniform temperature distribution

Thermoelectric generator (TEG) systems are gaining much attraction due to utility in heat recovery, surface cooling, concentrated solar thermal, and sensor applications. In series–parallel configurations, TEG modules due to Heterogeneous temperature difference (HeTD) show the non-linear behavior. Due to non-uniform temperature distribution (NUTD), multiple maximum power points (MPP) appears on the P-V curve. It is crucial to drive the system at true global maximum power point (GMPP) among multiple MPP’s. Existing classical techniques exhibit slow tracking, low efficiency, and undesired fluctuation in output voltage transients. To address these shortcomings our control technique based on improved Moth Flame Optimization (IMFO) is employed for the maximum power point tracking (MPPT) control under dynamic operating conditions. Comparison of the proposed technique is made with other well-known meta-heuristic techniques including particle swarm optimization (PSO), cuckoo search (CS), Artificial Bee Colony (ABC), and recently developed Dragon Fly Optimization (DFO). The comprehensive case studies with statistical and quantitative analysis are performed to confirm the superior performance of IMFO for NUTD condition, fast varying temperature condition, and stochastic operations. To experimentally validate the performance of the IMFO algorithm a low-cost TEG emulator setup is designed. The IMFO based control is implemented on a low-cost microcontroller achieving effective real-time control application in hardware. The proposed IMFO algorithm attains up to 6 W more power and takes 59% less time to track and settle at GMPP with minimum fluctuation. Results also validate that IMFO extracts 5.2% more electrical energy in comparison to competing techniques. In light of comprehensive analysis, it is safe to conclude that the proposed IMFO performs excellently for TEG MPPT control.

Classification of abnormal location in medium voltage switchgears using hybrid gravitational search algorithm-artificial intelligence.

In power system networks, automatic fault diagnosis techniques of switchgears with high accuracy and less time consuming are important. In this work, classification of abnormal location in switchgears is proposed using hybrid gravitational search algorithm (GSA)-artificial intelligence (AI) techniques. The measurement data were obtained from ultrasound, transient earth voltage, temperature and sound sensors. The AI classifiers used include artificial neural network (ANN) and support vector machine (SVM). The performance of both classifiers was optimized by an optimization technique, GSA. The advantages of GSA classification on AI in classifying the abnormal location in switchgears are easy implementation, fast convergence and low computational cost. For performance comparison, several well-known metaheuristic techniques were also applied on the AI classifiers. From the comparison between ANN and SVM without optimization by GSA, SVM yields 2% higher accuracy than ANN. However, ANN yields slightly higher accuracy than SVM after combining with GSA, which is in the range of 97%-99% compared to 95%-97% for SVM. On the other hand, GSA-SVM converges faster than GSA-ANN. Overall, it was found that combination of both AI classifiers with GSA yields better results than several well-known metaheuristic techniques.

Metaheuristics for the multi-task simultaneous supervision dual resource-constrained scheduling problem

This comprehensive study develops advantageous optimization methods to solve a nascent problem, namely multi-task simultaneous supervision dual resource-constrained (MTSSDRC) scheduling. MTSSDRC is a complex problem that deals with machine assignment, job sequencing, operator allocation, and task sequencing. Setup and unloading must be scheduled to operators, and they are allowed to leave machines while processing jobs. Earlier research on MTSSDRC developed a permutation-based genetic algorithm (PGA) with a specific decoding scheme, namely DSE, to solve the problem. Many previous studies succeed in solving scheduling problems by modifying well-known metaheuristic techniques. Therefore, we are inspired by this to explore further modifications to particular metaheuristics. The first contribution of the present study lies in the development of new decoding schemes that can perform better than the existing option. Five new decoding schemes are considered. Two of those schemes, namely DS2 and DS4, perform significantly better than DSE, reaching 6% relative deviation. DS4 is superior in terms of solution quality, but DS2 can run eight times faster. Another contribution is the development of six modified metaheuristics that are implemented for the MTSSDRC problem: tabu search, simulated annealing, particle swarm optimization, bees algorithm (BA), artificial bee colony, and grey wolf optimization. The performance of these metaheuristics is compared with that of the PGA. The results show that the PGA and BA are consistently superior for medium- and large-sized problems. The BA is more promising in terms of solution quality, but the PGA is faster.

Optimal control of a service system with emergency vacation using bat algorithm

The novelty of this research article is to study the effect of new introduced emergency vacation of a single service provider in service system via queueing-theoretic approach and comparative analysis of some metaheuristic and heuristic optimization techniques for optimal control. According to this vacation policy, the working state server takes a vacation in an emergency without completing the ongoing service of the waiting customer in the system. For the modeling purpose, it is assumed that the inter-arrival times, service times and vacation times are exponentially distributed. A cost optimization problem is developed to obtain the optimal values of system design parameters. The comparative analysis of proposed metaheuristic technique bat algorithm (BA) with another well-known metaheuristic technique: particle swarm optimization (PSO), and heuristic technique: Quasi-Newton method has been done to achieve the optimal operating conditions with minimal expected cost. Finally, numerical simulations and illustrations are provided to get an understanding of the mathematical modeling in detail and performance analysis of the queueing measures is also done. Numerical results are summarized in tables and graphs to provide a quick insight into critical issues related to the studied model. Concluding remarks are also drawn along with the future scope of the present study.

Binary spotted hyena optimizer and its application to feature selection

Spotted hyena optimizer (SHO) is a recently developed metaheuristic technique that mimics the hunting behavior of the spotted hyenas. However, it does not provide optimal solution for discrete problems. Therefore, a novel binary version of Spotted Hyena Optimizer is proposed in this paper. The binary version of SHO can deal with discrete optimization problems. In the proposed algorithm, tangent hyperbolic function is utilized to squash the continuous position and then these values are used to update the position of spotted hyenas. The prey searching, encircling, and attacking are three main steps of binary spotted hyena optimizer. The proposed algorithm has been compared with six well-known metaheuristic techniques over 29 benchmark test functions. The effects of convergence, scalability, and control parameters have been investigated. The statistical significance of the proposed approach has also been examined through ANOVA test. The proposed approach is also applied on feature selection domain. The performance of proposed approach has been compared with four well-known metaheuristic techniques over eleven UCI repository datasets. The experimental results reveal that the proposed approach is able to search the optimal feature set than the others.

Log Transform Based Optimal Image Enhancement Using Firefly Algorithm for Autonomous Mini Unmanned Aerial Vehicle: An Application of Aerial Photography

The Unmanned Aerial Vehicles (UAV) are widely used for capturing images in border area surveillance, disaster intensity monitoring, etc. An aerial photograph offers a permanent recording solution as well. But rapid weather change, low quality image capturing equipments results in low/poor contrast images during image acquisition by Autonomous UAV. In this current study, a well-known meta-heuristic technique, namely, Firefly Algorithm (FA) is reported to enhance aerial images taken by a Mini Unmanned Aerial Vehicle (MUAV) via optimizing the value of certain parameters. These parameters have a wide range as used in the Log Transformation for image enhancement. The entropy and edge information of the images is used as an objective criterion for evaluating the image enhancement of the proposed system. Inconsistent with the objective criterion, the FA is used to optimize the parameters employed in the objective function that accomplishes the superlative enhanced image. A low-light imaging has been performed at evening time to prove the effectiveness of the proposed algorithm. The results illustrate that the proposed method has better convergence and fitness values compared to Particle Swarm Optimization. Therefore, FA is superior to PSO, as it converges after a less number of iterations.

Solving Non-Smooth Optimal Power Flow Problems Using a Developed Grey Wolf Optimizer

The optimal power flow (OPF) problem is a non-linear and non-smooth optimization problem. OPF problem is a complicated optimization problem, especially when considering the system constraints. This paper proposes a new enhanced version for the grey wolf optimization technique called Developed Grey Wolf Optimizer (DGWO) to solve the optimal power flow (OPF) problem by an efficient way. Although the GWO is an efficient technique, it may be prone to stagnate at local optima for some cases due to the insufficient diversity of wolves, hence the DGWO algorithm is proposed for improving the search capabilities of this optimizer. The DGWO is based on enhancing the exploration process by applying a random mutation to increase the diversity of population, while an exploitation process is enhanced by updating the position of populations in spiral path around the best solution. An adaptive operator is employed in DGWO to find a balance between the exploration and exploitation phases during the iterative process. The considered objective functions are quadratic fuel cost minimization, piecewise quadratic cost minimization, and quadratic fuel cost minimization considering the valve point effect. The DGWO is validated using the standard IEEE 30-bus test system. The obtained results showed the effectiveness and superiority of DGWO for solving the OPF problem compared with the other well-known meta-heuristic techniques.

Improved simple optimization (SOPT) algorithm for unconstrained non-linear optimization problems

Summary In the recent years, population based meta-heuristic are developed to solve non-linear optimization problems. These problems are difficult to solve using traditional methods. Simple optimization (SOPT) algorithm is one of the simple and efficient meta-heuristic techniques to solve the non-linear optimization problems. In this paper, SOPT is compared with some of the well-known meta-heuristic techniques viz. Artificial Bee Colony algorithm (ABC), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Differential Evolutions (DE). For comparison, SOPT algorithm is coded in MATLAB and 25 standard test functions for unconstrained optimization having different characteristics are run for 30 times each. The results of experiments are compared with previously reported results of other algorithms. Promising and comparable results are obtained for most of the test problems. To improve the performance of SOPT, an improvement in the algorithm is proposed which helps it to come out of local optima when algorithm gets trapped in it. In almost all the test problems, improved SOPT is able to get the actual solution at least once in 30 runs.

Optimal Spectrum Access for Users in Multi-Channel Cognitive Radio Networks

Cognitive Radio, a software defined rad io, is an emerging technology which uses opportunistic spectrum access (OSA) for the efficient utilizat ion of the spectrum. Its main aim is to utilize the unused portions of the spectrum by allo wing unlicensed users (secondary users) to transmit data in absence of primary users. In this paper the assignment of mu ltip le primary and secondary users in a mult iple channel system is addressed with the sole aim of optimizing the total throughput. We have considered an optimal spectrum access (OSA) algorith m for the allot ment of both the primary and secondary users in a mult iple channel system and compared the results with the well-known meta-heuristic techniques such as Simulated Annealing (SA) and Tabu Search (TS). The results reveal that OSA algorithm is mo re efficient as compared to SA and TS as far as throughput optimization is considered.

Discrete size optimization of steel trusses using a refined big bang–big crunch algorithm

This article presents a methodology that provides a method for design optimization of steel truss structures based on a refined big bang–big crunch (BB-BC) algorithm. It is shown that a standard formulation of the BB-BC algorithm occasionally falls short of producing acceptable solutions to problems from discrete size optimum design of steel trusses. A reformulation of the algorithm is proposed and implemented for design optimization of various discrete truss structures according to American Institute of Steel Construction Allowable Stress Design (AISC-ASD) specifications. Furthermore, the performance of the proposed BB-BC algorithm is compared to its standard version as well as other well-known metaheuristic techniques. The numerical results confirm the efficiency of the proposed algorithm in practical design optimization of truss structures.

An exponential big bang-big crunch algorithm for discrete design optimization of steel frames

In the present study an enhanced variant of the big bang-big crunch (BB-BC) technique, namely exponential BB-BC algorithm (EBB-BC) is developed for code based design optimization of steel frame structures. It is shown that the standard version of the BB-BC algorithm is sometimes unable to produce reasonable solutions to problems from discrete design optimization of steel frames. Hence, through investigating the shortcomings of BB-BC algorithm, it is aimed to reinforce the performance of the technique for this class of problems in particular. The study provides numerical evidences of how the performance of a well-known metaheuristic technique (BB-BC) can significantly be improved through simple yet effective modifications in its formulation to achieve a more robust and reliable variant (EBB-BC). Further, a comparison of the optimum solutions with those of other well-known metaheuristics reveals that the EBB-BC algorithm can efficiently be used as a novel tool for code based design optimization of steel frames.

A simple and fast heuristic for protein structure comparison

BackgroundProtein structure comparison is a key problem in bioinformatics. There exist several methods for doing protein comparison, being the solution of the Maximum Contact Map Overlap problem (MAX-CMO) one of the alternatives available. Although this problem may be solved using exact algorithms, researchers require approximate algorithms that obtain good quality solutions using less computational resources than the formers.ResultsWe propose a variable neighborhood search metaheuristic for solving MAX-CMO. We analyze this strategy in two aspects: 1) from an optimization point of view the strategy is tested on two different datasets, obtaining an error of 3.5%(over 2702 pairs) and 1.7% (over 161 pairs) with respect to optimal values; thus leading to high accurate solutions in a simpler and less expensive way than exact algorithms; 2) in terms of protein structure classification, we conduct experiments on three datasets and show that is feasible to detect structural similarities at SCOP's family and CATH's architecture levels using normalized overlap values. Some limitations and the role of normalization are outlined for doing classification at SCOP's fold level.ConclusionWe designed, implemented and tested.a new tool for solving MAX-CMO, based on a well-known metaheuristic technique. The good balance between solution's quality and computational effort makes it a valuable tool. Moreover, to the best of our knowledge, this is the first time the MAX-CMO measure is tested at SCOP's fold and CATH's architecture levels with encouraging results.Software is available for download at .