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There is dependably an extraordinary requirement for new types of algorithms in the population-based improvement algorithm. These algorithms improve the execution of the current algorithm. Parameter change approach assumes an essential job in improving the execution of the PSO algorithm. A new algorithm called particle acceleration-based particle swarm optimization (PA-PSO) has been proposed. In this algorithm a particle acceleration parameter is tuned. This algorithm significantly improves the performance of the PSO-time varying acceleration coefficients (PSO-TVAC) algorithm. This algorithm reduces the time varying weight of inertia and the nonlinear acceleration coefficients in the equation of the PSO-TVAC velocity vector in each iteration. Particle movements in the n-dimensional search space are governed by the kinetics of the second motion equation. Experiments demonstrate that the proposed PA-PSO algorithm outperforms the existing PSO-TVAC algorithm on five well-known reference test functions. The algorithm possesses adequate control over the local as well as global optimums. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) are search algorithms based on population genetics and natural-selection concepts. Maintaining population variety in GAs is critical for ensuring global exploration and mitigating the risks of premature convergence. Rapid convergence to local optima is one such challenge in the application of genetic algorithms. To address this issue, we provide Cave-Surface GA (CSGA), an alternative method based on the Dual Population GA and inspired by the genetic variety observed in Mexican cavefish. Through inter-population cross-breeding, CSGA increases diversity via a secondary population (cave population) and facilitates the exchange of information between populations, effectively counteracting premature convergence. Several experiments are carried out utilizing benchmark instances of the Traveling Salesman Problem (TSP) obtained from TSPLIB, a well-known TSP problem library. Our experimental results over many TSP instances show that CSGA outperforms both classic GAs and other GAs that use diversity-preservation techniques, such as Multipopulation GA (MPGA). CSGA has the potential to give promising solutions to challenging optimization issues like TSP. [ABSTRACT FROM AUTHOR]

The Chimp Optimization Algorithm (ChOA) has emerged as a highly efficient optimization technique, demonstrating its prowess across diverse problem domains. However, its reliance on local search methods presents vulnerabilities, such as diminished diversity, susceptibility to premature convergence, and local minima. Thus, this study proposes two versions of enhancement the basic version of ChOA. The first version called ChOAO, integrates Opposition-based learning (OBL) to foster superior solution selection. The second version called ChOAORL, utilizes the concept of Reinforcement Learning (RL) to enhance the local search capabilities of ChOAO. It also effectively mitigates the risk of trapping the algorithm in local optima. The proposed versions are assessed utilizing the Friedman rank test on two sets of benchmark functions CEC 2017 and real-world problems CEC 2011. The results illustrate that ChOAORL achieved the best rank using CEC 2017 in both dimensions, 10 with a 1.48 mean rank and 30 with a 1.42 mean rank. Also, it outperformed other similar algorithms in terms of convergence precision and stability in all CEC 2011 real problems. [ABSTRACT FROM AUTHOR]

Deep Belief Network (DBN) and some other Artificial Neural Network (ANN) have been used for the detection and classification of diseases in plant but have been known for over-fitting problem. This has often times affected the accuracy of the system with high false positive rate. Particle Swarm Optimization (PSO) technique, has been used to enhance some deep learning techniques but still suffers stagnation in local optima and high computational cost mainly due to the large search space. Hence, there is need for improvement to develop a more efficient model for plant diseases detection and classification using Deep Belief Network with Improved Particle Swarm Optimization (DBN-IPSO). Images of healthy and unhealthy 3852 maize plant samples were acquired from https:\\www.kaggle.com. The acquired data were pre-processed, leaf colour images were converted to grayscale, cropped and contrast-enhanced with local histogram equalization. IPSO was formulated by incorporating modified acceleration coefficient and velocity component into the standard PSO to avoid premature convergence and imbalance between exploration and exploitation stages. The PSO was applied to DBN to select its optimal weight parameters. The system was implemented using a language app designer, trained and tested using k-fold cross-validation method, where k is 10-fold. The performance of the developed DBN-IPSO technique was compared with existing DBN-PSO and DBN using False Positive Rate (FPR), sensitivity, specificity, overall accuracy and computation time. The result shows that the developed technique improves the accuracy and efficiency of the system, making it a promising approach for automated plant disease diagnosis and surveillance in agriculture.

Genetic algorithms (GAs) are search algorithms based on population genetics and natural selection concepts. Maintaining population variety in GAs is critical for ensuring global exploration and mitigating the risks of premature convergence. Rapid convergence to local optima is one such challenge in the application of genetic algorithms. To address this issue, we provide Cave-Surface GA (CSGA), an alternative method based on the Dual Population Genetic Algorithm and inspired by the genetic variety observed in Mexican cavefish. Through inter-population crossbreeding, CSGA increases diversity via a secondary population (Cave population) and facilitates the exchange of information between populations, effectively counteracting premature convergence. Several experiments are carried out utilizing benchmark instances of the Traveling Salesman Problem (TSP) obtained from TSPLIB, a well-known TSP problem library. Our experimental results over many TSP instances show that CSGA outperforms both classic GAs and other GAs that use diversity preservation techniques, such as Multipopulation GA (MPGA). CSGA has the potential to give promising solutions to challenging optimization issues like TSP. [JJCIT 2024; 10(3.000): 231-246]

The PID controller is the most widely used controller, and the performance of each controller depends on its three gain parameters. The genetic algorithm (GA) is an optimization process that involves three types of operators: selection, crossover, and mutation. Inspired by natural evolution, a GA searches for the optimum solution automatically from a collection of potential solutions. The GA was employed for the optimization of PID controller parameters in this paper; this algorithm evolves from generation to generation until the stopping criterion is met or the optimal solution is found. The traditional GA has several shortcomings, such as the great influence of the best individual on the whole population, poor global search ability, and premature convergence; therefore, an improved cellular genetic algorithm (CGA) was proposed whose genetic operators occur between the individual and its neighbors. CGA avoids the adverse influence of the best individual and the possibility of falling into a local optimum. The simulation results show that CGA outperforms traditional GA. CGA is more efficient than GA in global search activities, and its optimum solution is better than that of GA. [ABSTRACT FROM AUTHOR]

Hyperspectral imaging effectively distinguishes land cover classes with discriminative spectral-spatial characteristics. However, hyperspectral image (HSI) classification encounters three significant challenges: the curse of dimensionality, the scarcity of training samples, and the necessity to incorporate spatial information within the classification process. The large number of redundant and irrelevant spectral bands in HSI can negatively impact the classification performance. Furthermore, the annotation process for training samples is expensive and time-consuming. To address these challenges, this work presents a novel semi-supervised approach for hyperspectral image dimensionality reduction using the Mayfly Algorithm (MA). The proposed method aims to preserve the most informative spectral bands while leveraging both labelled and unlabelled samples in the process. The MA algorithm effectively balances exploration and exploitation strategies, enhancing population diversity and mitigating challenges such as premature convergence and local optima. Additionally, the inclusion of structural variation and thresholding (SVT) in the Bitonic filter enables the incorporation of spatial features during the classification phase. This improves the separability of pixels belonging to different objects and leads to significant improvements in classification performance. Finally, two machine learning classifiers, namely Random Forest (RF) and Support Vector Machine (SVM), are applied at the pixel level for hyperspectral image classification. Extensive experimental results on three real hyperspectral datasets demonstrate the superiority of the proposed approach compared to state-of-the-art algorithms like Harmony Search (HS), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and more with an average improvement of 3.2%. [ABSTRACT FROM AUTHOR]

Flexible job shop scheduling problem (FJSP) is the core decision-making problem of intelligent manufacturing production management. The Harris hawk optimization (HHO) algorithm, as a typical metaheuristic algorithm, has been widely employed to solve scheduling problems. However, HHO suffers from premature convergence when solving NP-hard problems. Therefore, this paper proposes an improved HHO algorithm (GNHHO) to solve the FJSP. GNHHO introduces an elitism strategy, a chaotic mechanism, a nonlinear escaping energy update strategy, and a Gaussian random walk strategy to prevent premature convergence. A flexible job shop scheduling model is constructed, and the static and dynamic FJSP is investigated to minimize the makespan. This paper chooses a twosegment encoding mode based on the job and the machine of the FJSP. To verify the effectiveness of GNHHO, this study tests it in 23 benchmark functions, 10 standard job shop scheduling problems (JSPs), and 5 standard FJSPs. Besides, this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company's FJSP. The optimized scheduling scheme demonstrates significant improvements in makespan, with an advancement of 28.16% for static scheduling and 35.63% for dynamic scheduling. Moreover, it achieves an average increase of 21.50% in the on-time order delivery rate. The results demonstrate that the performance of theGNHHO algorithm in solving FJSP is superior to some existing algorithms. [ABSTRACT FROM AUTHOR]

The main reasons for the success of using chaos maps in meta-heuristic algorithms are fast optimization of non-linear and nonconvex problems. One of these cases is the control of the natural frequencies of structures to prevent the destructive and dangerous phenomenon of resonance. Natural frequencies have useful information about the dynamic behavior of structures, and by applying dynamic constraints, a significant improvement is achieved in the optimal design of structural weight. Applying frequency limits with traditional and gradient-based methods is very difficult and time-consuming, and in most cases, the calculation process stops at local optima. Recent research shows that chaos maps play a major role in escaping from local optima and reaching global optima. By combining these maps with meta- heuristic algorithms, while avoiding premature convergence, the access to global optima is accelerated and improved, and the ideal state of balance between the exploration and exploitation stages is realized. Today, chaotic algorithms are widely accepted by researchers and are considered as a challenging topic. In a recent research, six chaotic metaheuristic algorithms have been investigated for the formation and improvement of results with the optimal design of truss structures. In this part the chaotic algorithms include Chaotic Water Evaporation Optimization (CWEO), Chaotic Tug-of-War Optimization (CTWO) and Chaotic Thermal Exchange Optimization (CTEO) are examined. [ABSTRACT FROM AUTHOR]

Particle swarmoptimization (PSO) is a stochastic computation technique that has become an increasingly important branch of swarmintelligence optimization. However, like other evolutionary algorithms, PSO also suffers from premature convergence and entrapment into local optima in dealing with complexmultimodal problems. Thus this paper puts forward an adaptive multi-updating strategy based particle swarm optimization (abbreviated as AMS-PSO). To start with, the chaotic sequence is employed to generate highquality initial particles to accelerate the convergence rate of the AMS-PSO. Subsequently, according to the current iteration, different update schemes are used to regulate the particle search process at different evolution stages. To be specific, two different sets of velocity update strategies are utilized to enhance the exploration ability in the early evolution stage while the other two sets of velocity update schemes are applied to improve the exploitation capability in the later evolution stage. Followed by the unequal weightage of acceleration coefficients is used to guide the search for the global worst particle to enhance the swarm diversity. In addition, an auxiliary update strategy is exclusively leveraged to the global best particle for the purpose of ensuring the convergence of the PSO method. Finally, extensive experiments on two sets of well-known benchmark functions bear out that AMS-PSO outperforms several state-of-the-art PSOs in terms of solution accuracy and convergence rate. [ABSTRACT FROM AUTHOR]

Software Cost estimation is a very important part of software development life cycle. An error in software cost (effort) estimation can cause a huge financial loss to the company. Generally, this cost of a software can be estimated by the expert who has a lot of experience in creating software, sometimes this is also done with the help of existing models that defines relationship between software size (input) and effort (output). These models are created by fixing the relationship between input and output parameters. However, there is always a need of perfect model which can improve the prediction ability of preexisting models. This model can be created by adding more suitable parameters and by tuning the performance of this model on existing value. In this paper, we have done some changes to very popular COCOMO) model and also perform tuning of parameters by a new version of PSO known as Adaptive and Intelligent Swarms based algorithm. The testing of this model is dome on 10 NASA projects. The experimental results prove that this new model has better estimation capabilities as compared to other existing cost estimations models. [ABSTRACT FROM AUTHOR]

The quantum-behaved particle swarm optimization (QPSO) algorithm, a variant of particle swarm optimization (PSO), has been proven to be an effective tool to solve various of optimization problems. However, like other PSO variants, it often suffers a premature convergence, especially when solving complex optimization problems. Considering this issue, this paper proposes a hybrid QPSO with dynamic grouping searching strategy, named QPSO-DGS. During the search process, the particle swarm is dynamically grouped into two subpopulations, which are assigned to implement the exploration and exploitation search, respectively. In each subpopulation, a comprehensive learning strategy is used for each particle to adjust its personal best position with a certain probability. Besides, a modified opposition-based computation is employed to improve the swarm diversity. The experimental comparison is conducted between the QPSO-DGS and other seven state-of-art PSO variants on the CEC'2013 test suit. The experimental results show that QPSO-DGS has a promising performance in terms of the solution accuracy and the convergence speed on the majority of these test functions, and especially on multimodal problems. [ABSTRACT FROM AUTHOR]

Moth-flame optimization (MFO) is a prominent problem solver with a simple structure that is widely used to solve different optimization problems. However, MFO and its variants inherently suffer from poor population diversity, leading to premature convergence to local optima and losses in the quality of its solutions. To overcome these limitations, an enhanced moth-flame optimization algorithm named MFO-SFR was developed to solve global optimization problems. The MFO-SFR algorithm introduces an effective stagnation finding and replacing (SFR) strategy to effectively maintain population diversity throughout the optimization process. The SFR strategy can find stagnant solutions using a distance-based technique and replaces them with a selected solution from the archive constructed from the previous solutions. The effectiveness of the proposed MFO-SFR algorithm was extensively assessed in 30 and 50 dimensions using the CEC 2018 benchmark functions, which simulated unimodal, multimodal, hybrid, and composition problems. Then, the obtained results were compared with two sets of competitors. In the first comparative set, the MFO algorithm and its well-known variants, specifically LMFO, WCMFO, CMFO, ODSFMFO, SMFO, and WMFO, were considered. Five state-of-the-art metaheuristic algorithms, including PSO, KH, GWO, CSA, and HOA, were considered in the second comparative set. The results were then statistically analyzed through the Friedman test. Ultimately, the capacity of the proposed algorithm to solve mechanical engineering problems was evaluated with two problems from the latest CEC 2020 test-suite. The experimental results and statistical analysis confirmed that the proposed MFO-SFR algorithm was superior to the MFO variants and state-of-the-art metaheuristic algorithms for solving complex global optimization problems, with 91.38% effectiveness. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) algorithms work beautifully with unimodal optimization problems but often get trapped in local optima, particularly in multi-modal problems. Due to the stagnation, premature convergence also takes place. Moreover, each swarm uses the same strategy for updating its velocity and position vector which is definitely not effective in solving different kinds of problems. This paper implements multiple movement strategies to speed up the convergence rate in unimodal problems and handles the stagnation problem by introducing a new variant of PSO named adaptive intelligence Particle Swarm Optimization (AI-PSO). In the classical PSO algorithm, each particle updates its velocity by checking the positions of the best particles, which were received from the group and its historical data. Further, this updated velocity is used to calculate the new position of each particle. This process has been repeated for several iterations until the swarm reaches the optimal solution. Unlike PSO, in which particles’ movement was fixed and was dependent on its interaction with other swarms, AI-PSO changes its movement strategy by collecting information from the current environment. We tried to improve the convergence of PSO by preventing it from sticking to local optima and by adding free movement towards the optimal solution when it is close to the optimum solution by defining the multiple strategies for activities in different situations. Particles that are moving in the wrong direction or start stagnating, i.e., their personal best do not improve for a fixed number of iterations, are directed towards new potential regions by defining a new movement strategy. AI-PSO algorithm has been created by improving the adaptability and intelligence of swarms in determining the path for the movement. A counter known as intelligence level is created and updated according to the action taken by the swarm. This counter variable defines the swarm’s intelligence level and guides its movement in unavoidable conditions. When the value of the counter goes minimum, the swarm assumes that it got stuck in local optima and needs to change the movement strategy. Similarly, if the counter reaches the max value, the swarm gains sufficient information to move freely toward the best solutions. The movement strategy is updated by checking the pbest of two consecutive iterations. If the pbest of the swarm is changing in every generation, we can conclude that the swarm is improving. In such a case, the intelligence level of the swarm is increased by one, and we wait for a few generations until the counter reaches the max value. When the counter reaches the maximum value, a new movement strategy is defined to allow the swarm to move freely to improve the convergence rate. The performance of the new variant AI-PSO is established by comparing it with other versions of PSO over 24 benchmark functions provided by Black-Box Optimization Benchmarking (BBOB 2013). Results show that the proposed variant performs better than different peer algorithms. [ABSTRACT FROM AUTHOR]

A genetic algorithm is one of the best optimization techniques for solving complex nature optimization problems. Different selection schemes have been proposed in the literature to address the major weaknesses of GA i.e., premature convergence and low computational efficiency. This article proposed a new selection operator that provides a better trade-off between selection pressure and population diversity while considering the relative importance of each individual. The average accuracy of the proposed operator has been measured by χ2 goodness of fit test. It has been performed on two different populations to show its consistency. Also, its performance has been evaluated on fourteen benchmark problems while comparing it with competing selection operators. Results show the effective performance in terms of two statistics i.e., less average and standard deviation values. Further, the performance indexes and the GA convergence show that the proposed operator takes better care of selection pressure and population diversity.

As the single sensor is not competent for the variation of the total amount and components of DOM in a large range, according to the cross-sensitivity between fiber SPR sensors with different structures, a measuring method of DOM components is proposed by combining the deep learning algorithm with the fiber SPR sensors based on the regulation of metal film thickness. We exploit an improved cooperative particle swarm optimization algorithm (ICPSO) aiming at the problem of particle diversity loss caused by premature convergence of particles which not only considers the optimization information of single particles, the global particles and particles in the groups, but also considers the proportion of shared information. Then, the ICPSO is used to optimize the weights and thresholds of back propagation neural network (BPNN) to establish ICPSO-BP network, so as to construct three classifiers consists of ICPSO-BP (wave length), ICPSO-BP (spectral width), ICPSO-BP (light intensity). By comprehensive training of the resonance wavelength, spectrum width and light intensity of SPR effect for the measured water, five DOM components (tyrosine protein, tryptophan protein, fulvic acid, dissolved microbial metabolites and humic acid) and their concentrations in four water samples, namely, Inner canal (A), Hongze lake (B), Park lake (C) and Campus lake (D), have been effectively predicted. The prediction accuracy is more than 80%, among them, the highest prediction rate of tryptophan protein and its concentration in Hongze lake (B) which can reach 86%. Therefore, the dynamic range of SPR measurement is effectively expanded and better measurement accuracy and sensitivity are maintained, which verifies the feasibility of the proposed method in DOM measuring and provide a new idea for DOM component testing.

Among the numerous indoor localization methods, Light-Detection-and-Ranging (LiDAR)-based probabilistic algorithms have been extensively applied to indoor localization due to their real-time performance and high accuracy. Nevertheless, these methods are challenged in symmetrical environments when tackling global localization and the robot kidnapping problem. In this paper, a novel hybrid method that combines visual and probabilistic localization results is proposed. Augmented Monte Carlo Localization (AMCL) is improved for position tracking continually. LiDAR-based measurements' uncertainty is evaluated to incorporate discrete visual-based results; therefore, a better diversity of the particle can be maintained. The robot kidnapping problem can be detected and solved by preventing premature convergence of the particle filter. Extensive experiments were implemented to validate the robustness and accuracy performance. Meanwhile, the localization error was reduced from 30 mm to 9 mm during a 600 m tour. [ABSTRACT FROM AUTHOR]

In high-dimensional gene expression data, selecting an optimal subset of genes is crucial for achieving high classification accuracy and reliable diagnosis of diseases. This paper proposes a two-stage hybrid model for gene selection based on clustering and a swarm intelligence algorithm to identify the most informative genes with high accuracy. First, a clustering-based multivariate filter approach is performed to explore the interactions between the features and eliminate any redundant or irrelevant ones. Then, by controlling for the problem of premature convergence in the binary Bat algorithm, the optimal gene subset is determined using different classifiers with the Monte Carlo cross-validation data partitioning model. The effectiveness of our proposed framework is evaluated using eight gene expression datasets, by comparison with other recently published algorithms in the literature. Experiments confirm that in seven out of eight datasets, the proposed method can achieve superior results in terms of classification accuracy and gene subset size. In particular, it achieves a classification accuracy of 100% in Lymphoma and Ovarian datasets and above 97.4% in the rest with a minimum number of genes. The results demonstrate that our proposed algorithm has the potential to solve the feature selection problem in different applications with high-dimensional datasets., Competing Interests: Declaration of competing interest The authors have no competing interests to declare that are relevant to the content of this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In view of the premature convergence of particle swarm optimization (PSO) that is often caused by the loss of diversity, an improved cooperative PSO (ICPSO) is proposed. The method can dynamically combine the optimum values of the particles themselves, the global particles and the optimum values in groups, use the current optimization stage to dynamically adjust the shared proportion of information and effectively fuse various reference information, which can obtain superior global and local optimization performance. Additionally, to improve the diversity of the algorithm, a dynamic adjustment method using the grouping coefficient r for the convergence rate is put forward. This method makes the algorithm have a more appropriate convergence rate while improving the convergence precision and enhancing the performance of the algorithm. Finally, the algorithm is used to optimize a neural network. The convergence condition and convergence rate of the algorithm are assessed by theoretical analysis and simulation experiments. The results show that ICPSO has more advantages in its diversity and the adjustment of the convergence rate compared to other related algorithms. Regarding neural network optimization, the training speed and optimization precision of the ICPSO-BP neural network are the highest, which has reached the best and average level of classification accuracy 98.5%, 96.3% for 20 iterations in Iris, and 98.7%, 95.1% in Wine. Its average iteration times score the best in five problems out of six. [ABSTRACT FROM AUTHOR]

Most state-of-the-art Multiobjective Evolutionary Algorithms (moeas) promote the preservation of diversity of objective function space but neglect the diversity of decision variable space. The aim of this article is to show that explicitly managing the amount of diversity maintained in the decision variable space is useful to increase the quality of moeas when taking into account metrics of the objective space. Our novel Variable Space Diversity-based MOEA (vsd-moea) explicitly considers the diversity of both decision variable and objective function space. This information is used with the aim of properly adapting the balance between exploration and intensification during the optimization process. Particularly, at the initial stages, decisions made by the approach are more biased by the information on the diversity of the variable space, whereas it gradually grants more importance to the diversity of objective function space as the evolution progresses. The latter is achieved through a novel density estimator. The new method is compared with state-of-art moeas using several benchmarks with two and three objectives. This novel proposal yields much better results than state-of-the-art schemes when considering metrics applied on objective function space, exhibiting a more stable and robust behavior. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is widely used to solve various optimization problems, such as robotics visual perception and intelligent control under uncertainties, due to its simple rules and easy implementation. However, the PSO has premature convergence in the optimization process, which will lead to inaccurate problems such as uncertainties of the control system. To improve PSOs performance, a self-regulating particle swarm optimization with mutation mechanism (SRM-PSO) is proposed in this paper. SRM-PSO combines the mutation mechanism, self-regulation and self-perception strategy. The mutation mechanism is introduced to generate trial particle moving in different directions to maintain population diversity. Self-regulation and self-perception enable particles to be updated in different ways for fast exploration and intelligent exploitation. To validate the effectiveness of the SRM-PSO, experiments are conducted in the CEC2017 test suite. The test results indicate that SRM-PSO outperforms two related variants, and five representative PSO variants. Further, SRM-PSO is applied to several real-world optimization problems, which demonstrates its potential and competitiveness. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (PSO) is a popular stochastic approach for solving practical optimal problems from industries due to its effective performance and few hyperparameters. Nonlinear constrained optimization (NCO) problems frequently cause multiple optimal regions and can cause many infeasible regions in the search space. The state-of-the-art approaches for handling the infeasible regions generated by problems’ constraints either block particles’ paths or penalize NCO problems’ objective values based on the standard updating velocity formula. The standard updating velocity formula introduces difficulties for particles in searching the undiscovered optimal solutions separated by infeasible regions and being mutually restrained on directions by social and cognitive factors. Afterward, the particles cause premature convergence and difficulty searching the undiscovered optimal regions to improve their solutions. Observing the biological ant colony and inspired by lazy ant behavior, this study proposes an easy particle that simulates the lazy ant to diversify the moving direction. Finally, this study integrates the proposed easy particles with referenced PSO-based approaches for solving NCO problems. The experiment results show that the proposed easy particles can effectively reinforce exploration abilities and improve the performances of all referenced PSO-based algorithms to reduce the status of premature convergence in solving NCO problems.

Industrial robots were modified over the years. The benefit of robots is making production systems more efficient. Most methods of controlling robots have some limitations, such as stopping the robots. The robot stops by various reasons, such as collisions. The goal of this study is to study the comparison of improving the Artificial Potential Field (APF) by the traditional Particle Swarm Optimization (PSO) algorithm and the Serendipity-Based PSO (SBPSO) algorithm to control the path of a universal robot UR5 with collision avoidance. Already, the metaheuristic algorithm kinds deal with a premature convergence. This paper presents a new approach, which depends on the concept of serendipity and premature convergence applied to the path of the universal manipulator UR5 and also compares it with traditional the PSO. The features of the SBPSO algorithm prototype are formalized in this paper using the concept of serendipity in two dimensions: intelligence and chance. The results showed that the SBPSO is more efficient and has better convergence behavior than the traditional PSO for controlling the trajectory planning of the UR5 manipulator with obstacle avoidance. [ABSTRACT FROM AUTHOR]

Moth-flame optimization (MFO) is a prominent problem solver with a simple structure that is widely used to solve different optimization problems. However, MFO and its variants inherently suffer from poor population diversity, leading to premature convergence to local optima and losses in the quality of its solutions. To overcome these limitations, an enhanced moth-flame optimization algorithm named MFO-SFR was developed to solve global optimization problems. The MFO-SFR algorithm introduces an effective stagnation finding and replacing (SFR) strategy to effectively maintain population diversity throughout the optimization process. The SFR strategy can find stagnant solutions using a distance-based technique and replaces them with a selected solution from the archive constructed from the previous solutions. The effectiveness of the proposed MFO-SFR algorithm was extensively assessed in 30 and 50 dimensions using the CEC 2018 benchmark functions, which simulated unimodal, multimodal, hybrid, and composition problems. Then, the obtained results were compared with two sets of competitors. In the first comparative set, the MFO algorithm and its well-known variants, specifically LMFO, WCMFO, CMFO, ODSFMFO, SMFO, and WMFO, were considered. Five state-of-the-art metaheuristic algorithms, including PSO, KH, GWO, CSA, and HOA, were considered in the second comparative set. The results were then statistically analyzed through the Friedman test. Ultimately, the capacity of the proposed algorithm to solve mechanical engineering problems was evaluated with two problems from the latest CEC 2020 test-suite. The experimental results and statistical analysis confirmed that the proposed MFO-SFR algorithm was superior to the MFO variants and state-of-the-art metaheuristic algorithms for solving complex global optimization problems, with 91.38% effectiveness.

Differential evolution (DE) is recognized as a simplistic yet robust evolutionary algorithm: it has been utilized to tackle different challenging optimization problems in various science and engineering disciplines. DE has some disadvantages, such as premature convergence and slow convergence rate, leading to the worst DE execution arrangement. Two DE variations named adaptive parameter selection-based DE (APSDE) and chaotic map hybridization based on DE and PSO (CMHDE-PSO) have been proposed to tackle the issues mentioned above. The proposed variants contain three unique advantages for APSDE: (1) new population initialization scheme to keep up the decent variety of the population diversity; (2) controlled mutation factor technique followed by adaptive decreasing parameter selection procedure; (3) novel mutation strategy with a specific weighted pattern to determine the mutant vector for the mutation operation. Similarly, for CMHDE-PSO (1) novel distribution called Torus for the selection of initial population located in the search space; (2) new parameter adoption technique based on chaotic circle maps defined by chaos theory; (3) average pattern means of two different mutation strategies; (4) and lastly the hybridization of proposed improved DE with PSO to supports DE escaping local minima. Both APSDE and CMHDE-PSO are compared with several standard non-DE old-fashioned optimization algorithms and various advanced DE variants. We accomplish definite experiments behind the powerful searching technique by applying the APSDE and CMHDE-PSO-based mutation and parameter selection strategy for the function optimization and weight optimization of feed-forward neural networks (FFNN) on real-world data classification problems. For data classification performance evaluation, 10 data sets are utilized from the repository of UCI machine learning. Experimental results showed that APSDE and CMHDE-PSO extensively beat different EAs in all test functions and obtained higher accuracy with the recent state-of-the-art algorithms for weight optimization. [ABSTRACT FROM AUTHOR]

To solve different kinds of optimization challenges, meta-heuristic algorithms have been extensively used. Population initialization plays a prominent role in meta-heuristic algorithms for the problem of optimization. These algorithms can affect convergence to identify a robust optimum solution. To investigate the effectiveness of diversity, many scholars have a focus on the reliability and quality of meta-heuristic algorithms for enhancement. To initialize the population in the search space, this dissertation proposes three new low discrepancy sequences for population initialization instead of uniform distribution called the WELL sequence, Knuth sequence, and Torus sequence. This paper also introduces a detailed survey of the different initialization methods of PSO and DE based on quasi-random sequence families such as the Sobol sequence, Halton sequence, and uniform random distribution. For well-known benchmark test problems and learning of artificial neural network, the proposed methods for PSO (TO-PSO, KN-PSO, and WE-PSO), BA (BA-TO, BA-WE, and BA-KN), and DE (DE-TO, DE-WE, and DE-KN) have been evaluated. The synthesis of our strategies demonstrates promising success over uniform random numbers using low discrepancy sequences. The experimental findings indicate that the initialization based on low discrepancy sequences is exceptionally stronger than the uniform random number. Furthermore, our work outlines the profound effects on convergence and heterogeneity of the proposed methodology. It is expected that a comparative simulation survey of the low discrepancy sequence would be beneficial for the investigator to analyze the meta-heuristic algorithms in detail. [ABSTRACT FROM AUTHOR]

Among the numerous indoor localization methods, Light-Detection-and-Ranging (LiDAR)-based probabilistic algorithms have been extensively applied to indoor localization due to their real-time performance and high accuracy. Nevertheless, these methods are challenged in symmetrical environments when tackling global localization and the robot kidnapping problem. In this paper, a novel hybrid method that combines visual and probabilistic localization results is proposed. Augmented Monte Carlo Localization (AMCL) is improved for position tracking continually. LiDAR-based measurements’ uncertainty is evaluated to incorporate discrete visual-based results; therefore, a better diversity of the particle can be maintained. The robot kidnapping problem can be detected and solved by preventing premature convergence of the particle filter. Extensive experiments were implemented to validate the robustness and accuracy performance. Meanwhile, the localization error was reduced from 30 mm to 9 mm during a 600 m tour.

Particle swarm optimization (PSO) tends to be premature convergence due to easily trapping into local suboptimal areas. In order to overcome the PSO's defects, the reasons causing the defects are analyzed and summarized as population diversity deficiency, insufficient information sharing, unbalance of exploitation and exploration, and single update strategy. On this basis, inspired by human team collaboration behavior, a team collaboration particle swarm optimization (TCPSO) is proposed. Diversified updates strategies, dynamic grouping strategy, selectivity vector, and decreasing and increasing inertia weight are designed in TCPSO to solve the defects' reasons and improve the optimization performance. Eight typical test functions have been used to evaluate and compare the performance of different PSO variants, and the results have been proven that the optimal results found by TCPSO are better compared with other PSO variants, which demonstrates the rationality and effectiveness of TCPSO. Finally, a real-world problem for reliability optimization are solved by five algorithms, and the results prove the convergence rate and stable optimization performance of TCPSO, TCPSO can provide better support for reliability optimization of complex system.

Metaheuristic algorithms have been widely used to solve diverse kinds of optimization problems. For an optimization problem, population initialization plays a significant role in metaheuristic algorithms. These algorithms can influence the convergence to find an efficient optimal solution. Mainly, for recognizing the importance of diversity, several researchers have worked on the performance for the improvement of metaheuristic algorithms. Population initialization is a vital factor in metaheuristic algorithms such as PSO and DE. Instead of applying the random distribution for the initialization of the population, quasirandom sequences are more useful for the improvement the diversity and convergence factors. This study presents three new low-discrepancy sequences named WELL sequence, Knuth sequence, and Torus sequence to initialize the population in the search space. This paper also gives a comprehensive survey of the various PSO and DE initialization approaches based on the family of quasirandom sequences such as Sobol sequence, Halton sequence, and uniform random distribution. The proposed methods for PSO (TO-PSO, KN-PSO, and WE-PSO) and DE (DE-TO, DE-WE, and DE-KN) have been examined for well-known benchmark test problems and training of the artificial neural network. The finding of our techniques shows promising performance using the family of low-discrepancy sequences over uniform random numbers. For a fair comparison, the approaches using low-discrepancy sequences for PSO and DE are compared with the other family of low-discrepancy sequences and uniform random number and depict the superior results. The experimental results show that the low-discrepancy sequences-based initialization performed exceptionally better than a uniform random number. Moreover, the outcome of our work presents a foresight on how the proposed technique profoundly impacts convergence and diversity. It is anticipated that this low-discrepancy sequence comparative simulation survey would be helpful for studying the metaheuristic algorithm in detail for the researcher. [ABSTRACT FROM AUTHOR]

Differential evolution (DE) is a well-known evolutionary algorithm with simple operation and excellent performance, which has been applied to solve various optimization problems. To alleviate the problem of premature convergence or population stagnation faced by DE algorithm, this paper proposes an adaptive regeneration framework based on search space adjustment (ARSA), which can be easily embedded into various DE variants. When one individual cannot get improved for several generations, the ARSA framework will be triggered to randomly generate a substitute individual from a dynamic search space which is determined by a given adjustment mechanism controlled by two different levels of parameters. The trigger condition for each individual is adaptively controlled by its status in the current population. The space adjustment mechanism contains two strategies, one focuses on global exploration while the other on local exploitation. Moreover, the ARSA framework does not add any parameters that need to be pre-set, and all the included parameters are adaptive. To verify the availability of ARSA framework for solving complex optimization problems, thirty functions with different dimensions from IEEE CEC 2017 test platform and three real-life problems are employed for comparative experiments. The experimental results indicate that our ARSA framework notably improves the performance of two basic DE algorithms and six state-of-the-art DE variants. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) is a computational method in which a group of particles moves in search space in search of an optimal solution. During this movement, each particle updates its position and velocity with its best previous position and best position found by the swarm. Though PSO is considered as a potential solution and applied in many areas, it suffers from premature convergence in which all the particles are converged too early, resulting in sub-optimal results. Although there are several techniques to address premature convergence, achieving a higher convergence rate while avoiding premature convergence is still challenging. In this paper, we present two new memory-based variants of PSO for preventing premature convergence. The first technique (PSOMR), augments memory by leveraging the concepts of the Ebbinghaus forgetting curve. The second technique (MS-PSOMR) divides swarm into multiple subswarms. Both techniques use memory to store promising historical values and use them later to avoid premature convergence. The proposed approaches are compared with existing algorithms belonging to a similar category and evaluations on CEC 2010 and CEC 2017 benchmark functions. The results show that both the approaches performed significantly better for the measured metrics and discouraged premature convergence. [ABSTRACT FROM AUTHOR]

Particle swarm optimization (termed as PSO) is a metaheuristic algorithm inspired by the swarm intelligence. Since its advent, PSO has been successfully applied to tackle various issues that are hard to optimize. Unfortunately, similar to other evolutionary computation, PSO is also difficult to get rid of the bad luck of premature convergence and local optimization, especially when dealing with complex multimodal issues. Hence, this work proposes a diversity-guided PSO with multi-level learning strategy (DPSO-MLS). To begin with, chaotic opposition-based learning (OBL) is used to generate well-distributed initial particles to speed up convergence process of DPSO-MLS. Subsequently, according to the current swarm diversity, the high-layer learning mechanism enables PSO to explore the entire search space from a global perspective via attractive and repulsive strategies, respectively. Next, according to average fitness value of entire swarm, the low-layer learning strategy fine-tunes the particles search from a local perspective to sustain its diversity. To be specific, in the context of the repulsion phase, the regular varying function (RVF) embedded update strategy is used to block potential local optima so as to continue exploring the potential search space when the particle's fitness is less than the average of swarm, otherwise an alternative mutation scheme is utilized to enrich the swarm diversity. Accordingly, the slowly varying function (SVF) embedded update strategy is applied when the particle's fitness is greater than the average of swarm, otherwise the worst-best example learning based mechanism is exploited to update the worst particle to improve the quality of swarm in the context of the attraction phase. To verify the effectiveness of DPSO-MLS, extensive experiments are conducted and the results show that our proposal is superior or highly competitive to several existing PSOs according to the robustness, convergence rate and solution accuracy. [ABSTRACT FROM AUTHOR]