Spider monkey optimization algorithm for constrained optimization problems

This paper concentrates on the recognition of a navigational algorithm, which can resolve path optimization challenges in minimum time. This navigational algorithm helps the mobile robot to avoid collision with obstacles. Implementation of spider monkey optimization (SMO) algorithm is carried out to explain the objectives of this paper. SMO algorithm is established upon the splitting-combination nature of spider monkeys. The proposed algorithm is examined in various terrains to determine the robustness and effectuality of the proposed algorithm. Simulation results confirmed that spider monkey optimization algorithm selects a path, which guides the robot to reach the target in minimum possible time. Subsequently, it decreases the computational cost, as the time for computation is minimized. In extension, we replicated the previously used environment, and the proposed algorithm is compared with the corresponding implemented algorithm. Our results demonstrate that Spider monkey optimization (SMO) provides better results on the basis of time by selecting a path with lesser complexity. To determine the adequacy of the prescribed standard route-outlining control design, the common element of this paper is the numerical simulation.

Spider monkey optimization (SMO) algorithm, which simulates the food searching behavior of a swarm of spider monkeys, is a new addition to the class of swarm intelligent techniques for solving unconstrained optimization problems. The purpose of this article is to study the performance of SMO after incorporating quadratic approximation (QA) operator in it. The proposed version is named as QA‐based spider monkey optimization (QASMO). An experimental study has been carried out to check the validity and applicability of QASMO. For validation purpose, the performance of QASMO is tested over a benchmark set of 46 scalable and nonscalable problems, and results are compared with the original SMO algorithm. In order to test the applicability of the proposed algorithm in solving real‐life optimization problems, one of the most challenging optimization problems, namely, Lennard–Jones (LJ) problem is considered. LJ clusters containing atoms from three to ten have been taken into consideration, and results are presented. To the best of our knowledge, this is the first attempt to apply SMO and its proposed variant on a real‐life problem. The results demonstrate that incorporation of QA in SMO has positive effects on its performance in terms of reliability, efficiency, and accuracy.

Spider monkey optimization (SMO) algorithm is a recently developed optimizer that is stimulated by the extraordinary social activities of spider monkeys known as fission–fusion social structure. The SMO is developed to find solution of difficult optimization problems in real world, which are difficult to solve by the available deterministic strategies. During the solution search process in SMO, perturbation rate plays very important role. The convergence rate of SMO is highly affected by it. Usually, perturbation rate is defined by a simple function that is linearly in nature. But some application has nonlinear nature, thus a nonlinear function may improve the outcomes of SMO. For that reason, a non linear function, namely sigmoidal function used to decide perturbation in SMO and proposed strategy named as sigmoidal SMO. The investigational outcomes show the superiority of the anticipated technique over other meta-heuristics.

This paper is made to compare the Genetic Algorithm (GA) and Spider Monkey Optimization (SMO) for Traveling Salesman Problem. The background of the selection of these two algorithms based on the resulting performance has been recognized in the literature. In this study, 33 experiment were conducted where each experiment on the dataset was carried out for 10 iterations and then the Average Tour Cost value was taken from all experiments carried out. Based on the Average Tour Cost test, GA has a better performance than SMO with a value of 30.91 and 30.94 (example in the Burma dataset14). However, in terms of testing a larger dataset, the results of ATC SMO values are better than GA with values of 46303,47 and 53592,29 (example in dataset d198). This paper also examines the comparison of the results of the Minimum Tour Cost between GA and SMO. Where SMO got 73% (Win 23 Dataset), GA 27% (Win 9 Dataset), and 3% had the same Minimum Tour Cost. From all these experiments, it can be concluded SMO is the best solution in solving TSP.

Particle swarm optimization (PSO) algorithms face performance challenges, which lean on improving solutions quality, speed-up, dealing with large-scale problems, and exploitation of computational resources. Parallelism represents a suitable paradigm for overcoming the PSO challenges. Spider monkey optimization (SMO) algorithm is a recent PSO algorithm. SMO is based on the principle of dividing the swarm into subgroups, which may decrease its speedup. In this paper, a multi-swarm-based parallel spider monkey optimization (PSMO- MS) is proposed for dealing with large-scale problems based on the multi-swarm mechanism. PSMO-MS is implemented using a synchronous master/slave parallel model. The performance of the proposed PSMO-MS is tested on two 2-dimensional problems (Dekkers and Aarts problem and Camel Back-6 Six Hump problem). The results show that PSMO-MS outperforms SMO in terms of execution time and produces comparable and better solution quality with a large-scale problem, as well as a high solutions' density.

Forecasting is considered an estimation of future values based on past data, and its importance lies in future planning. Necessity often calls for finding a model that logically describes the dynamic relationships connecting a single output series to one or more input series. In this research, daily time series data for several years of maximum and minimum temperatures were used as input variables, and evaporation as a response variable for the Baghdad governorate during the period. (2012-2022). Environmental and climatic data often suffer from problems such as heterogeneity and unreliability, which are results of the non-linearity of that type of data. It is possible to use the Transfer function model, denoted by the symbol TF, to model the causal relationship between the output variable and one or more input variables after synchronizing the data temporally to achieve its homogeneity. The aim of this research is to improve the forecasting of time series data, and one of the proposed methods for this is the hybrid model combining the TF model and the Spider Monkey Optimization (SMO) Algorithm, referred to as the TF-SMO hybrid model. The outputs of the TF model are utilized as inputs in the spider monkey algorithm to integrate linear and nonlinear hybrid effects for data processing, modeling, and improving forecasting accuracy. To improve modeling and forecasting results, the (SMO) Algorithm was used as a method for optimizing the modeling of non-linear data patterns and enhancing forecasting. The hybrid model TF-SMO was proposed, which is a hybrid model of the SMO algorithm as an optimization method with the TF model. As for the issue of data heterogeneity, the Time Stratified (TS) method was used to temporally stratify the data into hot and cold seasons. Where the data for the hot and cold seasons for maximum and minimum temperatures and evaporation were divided into two groups: the first group for the training period and the second group for the testing period, approximately 65% for training and approximately 35% for testing .In this research ,data similar to real data was generated with the same sample size for the hot and cold seasons for the transfer function. The results indicate that the hybrid model achieves better accuracy compared to the TF model. Therefore, it is possible to use the TF-SMO hybrid model structure for similar accuracy in forecasting climatic time series data.

This paper introduces an application of the dandelion optimization (DO) algorithm in antenna arrays. This is the first time that the DO algorithm has been used for optimizing antenna arrays. For antenna array optimization, sidelobe level (SLL) and deep nulls are key technical indicators. A lower SLL can improve the signal-to-noise ratio and reduce the impact of clutter signals outside the main beam. Deep nulls need to be aligned with the direction of interference to eliminate the influence of interference sources. The combination of the two can effectively improve the anti-interference ability of the entire system. Therefore, antenna arrays with ultra-low sidelobes and ultra-deep nulls are currently hot in the field of antenna array design and are also some of the key technologies needed to achieve modern high-performance radar systems. As a new type of evolutionary algorithm inspired by nature, the DO algorithm is inspired by the wind propagation behavior of dandelions in nature. This algorithm iteratively updates the population from three stages of ascent, descent, and landing, ultimately finding the optimal position. It has good optimization ability in solving complex problems such as those involving nonlinearity, discreteness, and non-convexity, and the antenna array pattern synthesis optimization problem belongs to multivariate nonlinear problems. Therefore, the DO algorithm can be effectively applied in the field of antenna array optimization. In this work, we use the following method to obtain an optimized pattern of a linear array with the lowest sidelobe level (SLL), null placement in particular directions, and a lower notch in particular directions: by controlling the antenna array’s element spacing and leaving the phase unchanged to optimize the current amplitudes and by controlling the excitation current and phase fixation of the antenna array and changing the element spacing. In the first and second examples, different algorithms are used to reduce the SLL of the antenna. In the first example, the DO algorithm reduces the SLL to −33.37 dB, which is 2.67 dB, 2.67 dB, 3.77 dB, 2.74 dB, and 2.52 dB lower than five other algorithms. In the second example, the SLL optimized by the DO algorithm is −42.56 dB, which is 5.04 dB and 1.48 dB lower than two other algorithms. In both examples, the DO algorithm reduces the SLL lower than other algorithms when the main lobe of the antenna is not significantly widened. Examples 3, 4, and 5 use the DO algorithm to optimize the amplitude of the current, generating deep nulls and deep notches in specific directions. In Example 3, the DO algorithm obtains a depth of nulls equal to −187.6 dB, which is 66.7 dB and 44.3 dB lower than that of the flower pollination algorithm (FPA) and the chaotic colony predation algorithm (CCPA), respectively. In Example 4, the deep null obtained by the DO algorithm is as low as −98.69 dB, which is 6.67 dB lower than the deep null obtained by the grey wolf optimization (GWO) algorithm. In Example 5, the deep notch obtained by the DO algorithm is as low as −63.1 dB, which is 6.4 dB and 1.9 dB lower than the spider monkey optimization (SMO) algorithm and the grasshopper optimization algorithm (GOA), respectively. The data prove that the DO algorithm produces deeper nulls and notches than other algorithms. The last two examples involve reducing sidelobe levels and generating deep nulls by optimizing the spacing between elements. In Example 5, the SLL obtained using the DO algorithm is −22.8766 dB, which is 0.1998 dB lower than the lowest SLL of −22.6768 dB among other algorithms. In Example 6, the SLL obtained using the DO algorithm is −20.1012 dB, and the null depth is −125.1 dB, which is 1.592 dB lower than the SLL obtained by the cat swarm optimization (CSO) algorithm and 19.1 dB lower than the deep null obtained by the GWO algorithm, respectively. In summary, the results of six simulation experiments indicate that the DO algorithm has better optimization ability in linear array optimization than other evolutionary algorithms.

Multiobjective optimization with ϵ-constrained method for solving real-parameter constrained optimization problems

Spider Monkey Optimization is the newest member of the Swarm Intelligence-based algorithm, which is motivated by the extraordinary behavior of Spider Monkeys. The SMO algorithm is a population-based stochastic metaheuristic. The SMO algorithm is well balanced for good exploration and exploitation most of the times. This paper introduces an improved strategy to update the position of solution in Local Leader Phase. The proposed algorithm named as Improved Spider Monkey Optimization (ISMO) algorithm. This method is developed to improve the rate of convergence. The ISMO algorithm tested over the benchmark problems and its superiority established with the help of statistical results.

Over the past few decades, swarm intelligence (SI)-based algorithms are performing well in the optimization field. Grey wolf optimizer (GWO) is a new addition for the class of SI-based algorithms. It mimes the social behaviour of grey wolves. In GWO, a solution is updated by incorporating the information from the first fittest, second fittest and third fittest solution of the search space. Half of the generations in GWO are dedicated to exploration and the rest half are used for exploitation. To enhance the exploration capability of GWO, this article proposes a modified variant of GWO. In the proposed method, the searching process is guided by an arbitrarily selected solution to the search space. The proposed strategy is termed as neighbourhood-inspired grey wolf optimizer (NIGWO). To validate the performance of NIGWO, 16 benchmark functions are considered and the results obtained are compared with the basic version of GWO, particle swarm optimization (PSO), power law-based local search in spider monkey optimization (PLSMO) and differential evolution (DE) algorithm. The obtained results validate the proposed NIGWO approach.KeywordsSwarm intelligenceGrey wolf optimizerNature-inspired algorithmOptimization

The global increase in greenhouse gas emissions from automobiles has brought about the manufacture and usage of large quantities of electric vehicles (EVs). However, to ensure proper integration of EVs into the grid, there is a need to forecast the charging demand of EVs accurately. This paper presents a short-term electric vehicle charging demand forecast using a feedforward artificial neural network optimized with a modified local leader phase spider monkey optimization (MLLP-SMO) algorithm, a proposed variant of spider monkey optimization. A proportionate fitness selection is employed to improve the update process of the local leader phase of the spider monkey optimization. The proposed algorithm trains a feedforward neural network to forecast electric vehicle charging demand. The effectiveness of the proposed forecasting model was tested and validated with electric vehicle public charging data from the United Kingdom Power Networks Low Carbon London Project. The model's performance was compared to a feedforward neural network trained with particle swarm optimization, genetic algorithm, classical spider monkey optimization, and two conventional forecasting models, multi-linear regression and Monte Carlo simulation. The performance of the proposed forecasting model was assessed using the mean absolute percentage error of forecast and forecasting accuracy. The model produced a forecast accuracy and mean absolute percentage error of 99.88% and 3.384%, respectively. The results show that MLLP-SMO as a trainer predicted better than the other forecasting models and met industry standard forecast accuracy.

Feature selection using competitive coevolution of bio-inspired algorithms for the diagnosis of pulmonary emphysema

In order to improve the solution accuracy of the lightning attachment algorithm in dealing with constrained optimization problem, an improved constrained lightning attachment algorithm is proposed in this paper. Firstly, the parameter setting of the constraint processing technology was improved, and the constraint violation degree of all individuals in the population was added to further distinguish between feasible individuals and infeasible individuals; Secondly, several stages in the lightning attachment procedure optimization algorithm are improved, including the performance enhancement stage, the uplink leader stage, the downlink leader stage and so on. The improved constrained lightning attachment procedure optimization algorithm proposed in this paper is compared with the other three constrained optimization algorithms on the standard test function g01-g13. The results show that the algorithm proposed in this paper has certain advantages in solving precision.

INTRODUCTION: The artificial bee colony (ABC) algorithm is a nature-inspired technique used for solving different optimization problems. This paper presents a multi-hive ABC algorithm for solving constrained benchmark functions of CEC2006. The CEC2006 data set contains the global benchmark functions with different design variables, number and type of constraints. OBJECTIVES: The objective of the proposed work is to design and apply the GPGPU based multi-hive ABC algorithm to solve constrained optimization problems. METHODS: The proposed approach is a multi-population coarse-grained system implemented using General Purpose Graphics Processing Unit (GPGPU). The performance of the proposed approach is compared with the serial ABC algorithm for eleven benchmark functions and results in the literature. The multi-hive ABC algorithm has multiple hives, each running separate ABC algorithm on different cores of GPGPU. RESULTS: The proposed approach provides global best solutions in significantly reduced time for all benchmark functions. The speed-up obtained is approximately 7X to 9X. The GPGPU device utilization is approximately 57% to 91%. CONCLUSION: The GPGPU based multi-hive ABC algorithm is a found good with respect to best results, speed up factor and GPU utilization to solve constrained optimization problems.

This paper presents dynamic performance comparison of a fuzzy logic-based proportional, integral, and derivative controller (FPID) with different membership functions such as triangular, trapezoidal, and Gaussian for load frequency control (LFC) in an interconnected two-area thermal power system. The parameters of controller are optimized by using spider monkey optimization (SMO) algorithm. The superiority of the proposed algorithm is established by comparing the results with popularly used algorithms like particle swarm optimization (PSO) and teaching–learning-based optimization (TLBO). Initially, the linearized model of the system is considered with reheat turbine; then, the study is extended by imposing nonlinearity such as generation rate constraints (GRC) and governor dead band (GDB). The result comparison is analyzed using various time domain specifications like peak undershoot, peak overshoot, and settling time of different area frequencies and tie-line power deviation between them applying a step load perturbation (SLP) of 1%.