Nature-inspired Swarm Research Articles

Lunarminer Framework for Nature-Inspired Swarm Robotics in Lunar Water Ice Extraction.

The Lunarminer framework explores the use of biomimetic swarm robotics, inspired by the division of labor in leafcutter ants and the synchronized flashing of fireflies, to enhance lunar water ice extraction. Simulations of water ice extraction within Shackleton Crater showed that the framework may improve task allocation, by reducing the extraction time by up to 40% and energy consumption by 31% in scenarios with high ore block quantities. This system, capable of producing up to 181 L of water per day from excavated regolith with a conversion efficiency of 0.8, may allow for supporting up to eighteen crew members. It has demonstrated robust fault tolerance and sustained operational efficiency, even for a 20% robot failure rate. The framework may help to address key challenges in lunar resource extraction, particularly in the permanently shadowed regions. To refine the proposed strategies, it is recommended that further studies be conducted on their large-scale applications in space mining operations at the Extraterrestrial Environmental Simulation (EXTERRES) laboratory at the University of Adelaide.

Classifying Nature-Inspired Swarm Algorithms for Sustainable Autonomous Mining

Classifying Nature-Inspired Swarm Algorithms for Sustainable Autonomous Mining

Choice Function-Based Hyper-Heuristics for Causal Discovery under Linear Structural Equation Models.

Causal discovery is central to human cognition, and learning directed acyclic graphs (DAGs) is its foundation. Recently, many nature-inspired meta-heuristic optimization algorithms have been proposed to serve as the basis for DAG learning. However, a single meta-heuristic algorithm requires specific domain knowledge and empirical parameter tuning and cannot guarantee good performance in all cases. Hyper-heuristics provide an alternative methodology to meta-heuristics, enabling multiple heuristic algorithms to be combined and optimized to achieve better generalization ability. In this paper, we propose a multi-population choice function hyper-heuristic to discover the causal relationships encoded in a DAG. This algorithm provides a reasonable solution for combining structural priors or possible expert knowledge with swarm intelligence. Under a linear structural equation model (SEM), we first identify the partial v-structures through partial correlation analysis as the structural priors of the next nature-inspired swarm intelligence approach. Then, through partial correlation analysis, we can limit the search space. Experimental results demonstrate the effectiveness of the proposed methods compared to the earlier state-of-the-art methods on six standard networks.

Multi-objective optimization of CMOS low noise amplifier through nature-inspired swarm intelligence

This paper presents the application of two swarm intelligence techniques, multi-objective artificial bee colony (MOABC) and multi-objective particle swarm optimization (MOPSO), to the optimal design of a complementary metal oxide semiconductor (CMOS) low noise amplifier (LNA) cascode with inductive source degeneration. The aim is to achieve a balanced trade-off between voltage gain and noise figure. The optimized LNA circuit operates at 2.4 GHz with a 1.8 V power supply and is implemented in a 180 nm CMOS process. Both optimization algorithms were implemented in MATLAB and evaluated using the ZDT1, ZDT2, and ZDT3 test functions. The optimized designs were then simulated using the advance design system (ADS) simulator. The results showed that the MOABC and MOPSO techniques are practical and effective in optimizing LNA design, resulting in better performance than previously published works, with a gain of 21.2 dB and a noise figure of 0.848 dB.

Early Diagnosis of Retinal Blood Vessel Damage via Deep Learning-Powered Collective Intelligence Models

Early diagnosis of retinal diseases such as diabetic retinopathy has had the attention of many researchers. Deep learning through the introduction of convolutional neural networks has become a prominent solution for image-related tasks such as classification and segmentation. Most tasks in image classification are handled by deep CNNs pretrained and evaluated on imagenet dataset. However, these models do not always translate to the best result on other datasets. Devising a neural network manually from scratch based on heuristics may not lead to an optimal model as there are numerous hyperparameters in play. In this paper, we use two nature-inspired swarm algorithms: particle swarm optimization (PSO) and ant colony optimization (ACO) to obtain TDCN models to perform classification of fundus images into severity classes. The power of swarm algorithms is used to search for various combinations of convolutional, pooling, and normalization layers to provide the best model for the task. It is observed that TDCN-PSO outperforms imagenet models and existing literature, while TDCN-ACO achieves faster architecture search. The best TDCN model achieves an accuracy of 90.3%, AUC ROC of 0.956, and a Cohen's kappa score of 0.967. The results were compared with the previous studies to show that the proposed TDCN models exhibit superior performance.

A powerful meta-heuristic search algorithm for solving global optimization and real-world solar photovoltaic parameter estimation problems

The teaching-learning-based artificial bee colony (TLABC) is a new hybrid swarm-based metaheuristic search algorithm. It combines the exploitation of the teaching learning-based optimization (TLBO) with the exploration of the artificial bee colony (ABC). With the hybridization of these two nature-inspired swarm intelligence algorithms, a robust method has been proposed to solve global optimization problems. However, as with swarm-based algorithms, with the TLABC method, it is a great challenge to effectively simulate the selection process. Fitness-distance balance (FDB) is a powerful recently developed method to effectively imitate the selection process in nature. In this study, the three search phases of the TLABC algorithm were redesigned using the FDB method. In this way, the FDB-TLABC algorithm, which imitates nature more effectively and has a robust search performance, was developed. To investigate the exploitation, exploration, and balanced search capabilities of the proposed algorithm, it was tested on standard and complex benchmark suites (Classic, IEEE CEC 2014, IEEE CEC 2017, and IEEE CEC 2020). In order to verify the performance of the proposed FDB-TLABC for global optimization problems and in the photovoltaic parameter estimation problem (a constrained real-world engineering problem) a very comprehensive and qualified experimental study was carried out according to IEEE CEC standards. Statistical analysis results confirmed that the proposed FDB-TLABC provided the best optimum solution and yielded a superior performance compared to other optimization methods.

On the binarization of Grey Wolf optimizer: a novel binary optimizer algorithm

Grey Wolf Optimizer (GWO) is a nature-inspired swarm intelligence algorithm that mimics the hunting behavior of grey wolves. GWO, in its basic form, is a real coded algorithm that needs modifications to deal with binary optimization problems. In this paper, previous work on the binarization of GWO are reviewed, and are classified with respect to their encoding scheme, updating strategy, and transfer function. Then, we propose a novel binary GWO algorithm (named SetGWO), which is based on set encoding and uses set operations in its updating strategy. The proposed algorithm uses a completely different encoding scheme that eliminates the need for the transfer function and boundary checking, and also uses lower-dimensional agents; therefore, decreases the running time. Also, by using an exclusive exploration set for each agent, defining a different distance measure and an encircling strategy in discrete spaces, the quality of solutions has been improved. Experimental results on different real-world combinatorial optimization problems and datasets show that SetGWO outperforms other existing binary GWO algorithms in terms of quality of solutions, running time, and scalability.

Implementing modified swarm intelligence algorithm based on Slime moulds for path planning and obstacle avoidance problem in mobile robots

Planning a collision-free path in the least processing time and cost within constraints is a central issue in designing an autonomous mobile robot (AMR). Nature-inspired swarm intelligence (NISI) metaheuristic algorithms are gaining popularity in path planning and obstacle avoidance (PPOA) problem in AMRs. An efficient PPOA algorithm’s objective encompasses the ability to read a workspace map and consequently create the shortest collision-free path for the robot to manoeuvre from start to goal in the least processing time and effort. The authors have implemented a modified NISI metaheuristic approach known as a Slime Mould Optimization Algorithm (SMOA) in this research. SMOA takes inspiration from the oscillatory nature of slime mould when it encounters prey. Its mathematical model utilizes adaptive weights to simulate an optimal path for capturing prey or food. The slime moulds produce positive and negative feedback while propagating towards food with excellent exploratory competency and exploitation propensity. For this, simulation has been carried out on MATLAB 2020a. Additionally, the performance of SMOA has been compared with other NISI metaheuristic approaches such as PSO, FA, SFLA and ABC. The results demonstrate that modified SMOA takes less time and effort to generate an optimal collision-free path as compared to other mentioned approaches.

A Customized Differential Evolutionary Algorithm for Bounded Constrained Optimization Problems

Bound-constrained optimization has wide applications in science and engineering. In the last two decades, various evolutionary algorithms (EAs) were developed under the umbrella of evolutionary computation for solving various bound-constrained benchmark functions and various real-world problems. In general, the developed evolutionary algorithms (EAs) belong to nature-inspired algorithms (NIAs) and swarm intelligence (SI) paradigms. Differential evolutionary algorithm is one of the most popular and well-known EAs and has secured top ranks in most of the EA competitions in the special session of the IEEE Congress on Evolutionary Computation. In this paper, a customized differential evolutionary algorithm is suggested and applied on twenty-nine large-scale bound-constrained benchmark functions. The suggested C-DE algorithm has obtained promising numerical results in its 51 independent runs of simulations. Most of the 2013 IEEE-CEC benchmark functions are tackled efficiently in terms of proximity and diversity.

Electricity Theft Detection With Automatic Labeling and Enhanced RUSBoost Classification Using Differential Evolution and Jaya Algorithm

Electricity theft has emerged as one of the major reasons of Non-Technical Losses (NTLs) in the power distribution systems and has become a global issue. Therefore, power utilities are concerned about resolving the issue of electricity theft. In this regard, the data collected by Advanced Metering Infrastructure (AMI) can be used to devise data-driven machine learning-based Electricity Theft Detection (ETD) methods. In this paper, a novel data-driven ETD method is proposed that firstly labels the electricity consumers as fair or malicious based on three analyses: intra-consumer, inter-consumer, and temperature-electricity consumption relation. After assigning labels to the data, significant features are extraction from data by introducing a new feature extractor that is based on Reconstruction Independent Component Analysis (RICA) and sparse auto-encoder. Finally, classification is performed using two newly proposed enhanced classifiers, named as Differential Evolution (DE) Random Undersampling Boosting (DE-RUSBoost) and Jaya-RUSBoost. The performance of RUSBoost is enhanced using two nature-inspired swarm intelligence-based optimization algorithms, namely DE and Jaya optimization. The performance evaluation of the proposed classifiers is performed by conducting comprehensive simulations on real-world data taken from the UMass* smart homes electricity consumption dataset and the State Grid Corporation of China (SGCC) electricity theft dataset. DE-RUSBoost achieves an Area Under the Curve (AUC) of 0.89 and Jaya-RUSBoost achieves AUC of 0.95. The proposed classifiers have superior performance compared to two state-of-the-art benchmarks, i.e., Wide And Deep Convolution Neural Network (WADCNN) and grid search-based RUSBoost.

Improving the Performance of Whale Optimization Algorithm through OpenCL-Based FPGA Accelerator

Whale optimization algorithm (WOA), known as a novel nature-inspired swarm optimization algorithm, demonstrates superiority in handling global continuous optimization problems. However, its performance deteriorates when applied to large-scale complex problems due to rapidly increasing execution time required for huge computational tasks. Based on interactions within the population, WOA is naturally amenable to parallelism, prompting an effective approach to mitigate the drawbacks of sequential WOA. In this paper, field programmable gate array (FPGA) is used as an accelerator, of which the high-level synthesis utilizes open computing language (OpenCL) as a general programming paradigm for heterogeneous System-on-Chip. With above platform, a novel parallel framework of WOA named PWOA is presented. The proposed framework comprises two feasible parallel models called partial parallel and all-FPGA parallel, respectively. Experiments are conducted by performing WOA on CPU and PWOA on OpenCL-based FPGA heterogeneous platform, to solve ten well-known benchmark functions. Meanwhile, other two classic algorithms including particle swarm optimization (PSO) and competitive swarm optimizer (CSO) are adopted for comparison. Numerical results show that the proposed approach achieves a promising computational performance coupled with efficient optimization on relatively large-scale complex problems.

Intrascale windowing-based cuckoo search–optimized sub-band thresholding for satellite image denoising

Traditional satellite image denoising techniques can minimize Gaussian noise efficiently, but they fail to preserve image feature and may cause blurring effect after denoising. To overcome such limitations, this paper proposes a new intrascale windowing/dependency-based sub-band adaptive thresholding approach for feature-preserved satellite image denoising using a cuckoo search algorithm. In this framework, basically, a 4 × 4 window has been created for each coefficient to find the local parameters in each sub-band instead of the global parameter. Therefore, the most widely used nature-inspired swarm and evolutionary algorithms are exploited via the intrascale dependency method for learning the parameters of adaptive thresholding function. It has been examined that the proposed windowing-based adaptive thresholding with cuckoo search (CS) algorithm yields better edge information and reveals superior outcomes in terms of peak signal-to-noise ratio (PSNR), mean square error (MSE), structure similarity index (SSIM), and feature similarity index (FSIM) fidelity parameters as compared with exiting classical techniques which are corrupted by Gaussian noise. In this paper, several well-recognized traditional (Weiner, median, Bayes, and soft and hard thresholds) denoising approaches are compared with the proposed method.

A multi-strategy enhanced salp swarm algorithm for global optimization

As a typical nature-inspired swarm intelligence algorithm, because of the simple framework and good optimization performance, salp swarm algorithm (SSA) has been extensively applied to a lot of practical problems. Nevertheless, when facing a number of complicated optimization problems, particularly the high dimensionality and multi-dimensional problems, SSA will come to stagnation and decrease the optimal performance. To tackle this problem, this paper presents an enhanced SSA (ESSA) in which several strategies, including orthogonal learning, quadratic interpolation, and generalized oppositional learning are embedded to boost the global exploration and local exploitation performance of SSA. Orthogonal learning can help the worse salp break away from local optima, while quadratic interpolation is utilized to improve the accuracy of the global optimal through local search near the globally optimal solution. Also, generalized oppositional learning is used to improve the population quality through the initialization step and generation jumping. These strategies work together to assist SSA in promoting convergence performance. At the last CEC2017 benchmark suite and CEC2011, a real-world optimization benchmark is employed to estimate the property of ESSA in dealing with the high dimensionality and multi-dimensional problems. Three constrained engineering optimization problems are also used to assess the capability of ESSA in tackling practical engineering application problems. The experimental results and responding analysis make clear that the presented algorithm significantly outperforms the original SSA and other state-of-the-art methods.

An Extended Bayesian Optimization Approach to Decentralized Swarm Robotic Search

Abstract Swarm robotic search aims at searching targets using a large number of collaborating simple mobile robots, with applications to search and rescue and hazard localization. In this regard, decentralized swarm systems are touted for their coverage scalability, time efficiency, and fault tolerance. To guide the behavior of such swarm systems, two broad classes of approaches are available, namely, nature-inspired swarm heuristics and multi-robotic search methods. However, the ability to simultaneously achieve efficient scalability and provide fundamental insights into the exhibited behavior (as opposed to exhibiting a black-box behavior) remains an open problem. To address this problem, this paper extends the underlying search approach in batch-Bayesian optimization to perform search with embodied swarm agents operating in a (simulated) physical 2D arena. Key contributions lie in (1) designing an acquisition function that not only balances exploration and exploitation across the swarm but also allows modeling knowledge extraction over trajectories and (2) developing its distributed implementation to allow asynchronous task inference and path planning by the swarm robots. The resulting collective informative path planning approach is tested on target-search case studies of varying complexity, where the target produces a spatially varying (measurable) signal. Notably, superior performance, in terms of mission completion efficiency, is observed compared to exhaustive search and random walk baselines as well as a swarm optimization-based state-of-the-art method. Favorable scalability characteristics are also demonstrated.

A new visual tracking approach based on salp swarm algorithm for abrupt motion tracking

Salp Swarm Algorithm (SSA) is a new nature-inspired swarm optimization algorithm that mimics the swarming behavior of salps navigating and foraging in the oceans. SSA has been proved to enable to avoid local optima and enhance convergence speed benefiting from the adaptive nonlinear mechanism and salp chains. In this paper, visual tracking is considered to be a process of locating the optimal position through the interaction between leaders and followers in successive images. A novel SSA-based tracking framework is proposed and the analysis and adjustment of parameters are discussed experimentally. Besides, the qualitative analysis and quantitative analysis are performed to demonstrate the tracking effect of our proposed approach by comparing with ten classical tracking algorithms. Extensive comparative experimental results show that our algorithm has good performance in visual tracking, especially for abrupt motion tracking.

Pareto optimality and game theory approach for optimal deployment of DG in radial distribution system to improve techno-economic benefits

This paper discusses the application of nature-inspired swarm intelligence methods for optimal allocation and sizing of distributed generation (DG) in the radial distribution system (RDS). Introducing DG units in the RDS will enhance the technical and economic benefits if they are optimally deployed. The objective functions considered are to improve the technical aspects and net economical saving cost with DG units integration on RDS. In this paper, a weighted multi-objective index considers a wide range of technical issues such as active and reactive power losses of the system, voltage profile, line loading, and the voltage stability, these are assumed as technical improvement aspects in the RDS. A recent optimization method, i.e. improved raven roosting optimization (IRRO) algorithm has been implemented for optimal deployment of DG in RDS. The state of the art of IRRO algorithm parameters will improve the ability for exploration and prevent premature convergence. Pareto optimality is used in making a set of the best solutions between two conflicting objectives considered, i.e. technical and economical aspects. The main contribution in this paper is to utilize a game theory based (minimax) algorithm in taking the best decision from a set of non-dominated solutions obtained by Pareto optimality criteria. IEEE 33-bus and 69-bus RDS’s are considered as the test systems for verifying the effectiveness of the IRRO algorithm. A comparative analysis with other nature-inspired swarm optimization techniques such as particle swarm optimization (PSO), modified teaching learning based optimization (MTLBO), Jaya algorithm (JAYA), and grey wolf optimizer (GWO) is also presented in this work. The simulation results of IRRO are compared with similar existing papers. It is observed that the IRRO algorithm can produce better results for the considered multi-objective functions. The MATLAB software is employed for the purpose. The novelty of the paper lies in the use of Pareto optimal and game theory in obtaining better results to the problem of optimal deployment of DG in RDS to improve technical as well as economic benefits.

Novel visual tracking approach via ant lion optimiser

Ant lion optimiser (ALO) is a new nature-inspired swarm intelligence optimisation algorithm that mimics the hunting mechanism of antlions in nature. ALO has been proved to have the merits of high exploitation and convergence speed benefiting from adaptive boundary shrinking mechanism and elitism. In this work, visual tracking is expressed as searching for object in whole search space by interaction between antlions and ants. A novel ALO-based visual tracking framework is proposed and the adaptation and sensitivity of the parameters in ALO are discussed to improve tracking performance. In addition, considering that ALO tracker needs a lot of iteration consumption, kernel correlation filter with deep feature is integrated into the ALO tracking framework (ALOKCF) to improve track efficiency. Extensive experimental results prove that the ALO tracker is very competitive compared to other trackers, especially for abrupt motion tracking. At the same time, two visual tracking benchmarks are used to verify ALOKCF tracker achieves state-of-the-art performance.

A Comparative Performance Study of Hybrid Firefly Algorithms for Automatic Data Clustering

In cluster analysis, the goal has always been to extemporize the best possible means of automatically determining the number of clusters. However, because of lack of prior domain knowledge and uncertainty associated with data objects characteristics, it is challenging to choose an appropriate number of clusters, especially when dealing with data objects of high dimensions, varying data sizes, and density. In the last few decades, different researchers have proposed and developed several nature-inspired metaheuristic algorithms to solve data clustering problems. Many studies have shown that the firefly algorithm is a very robust, efficient and effective nature-inspired swarm intelligence global search technique, which has been successfully applied to solve diverse NP-hard optimization problems. However, the diversification search process employed by the firefly algorithm can lead to reduced speed and convergence rate for large-scale optimization problems. Thus this study investigates the application of four hybrid firefly algorithms to the task of automatic clustering of high density and large-scaled unlabelled datasets. In contrast to most of the existing classical heuristic-based data clustering analyses techniques, the proposed hybrid algorithms do not require any prior knowledge of the data objects to be classified. Instead, the hybrid methods automatically determine the optimal number of clusters empirically and during the program execution. Two well-known clustering validity indices, namely the Compact-Separated and Davis-Bouldin indices, are employed to evaluate the superiority of the implemented firefly hybrid algorithms. Furthermore, twelve standard ground truth clustering datasets from the UCI Machine Learning Repository are used to evaluate the robustness and effectiveness of the algorithms against those of the classical swarm optimization algorithms and other related clustering results from the literature. The experimental results show that the new clustering methods depict high superiority in comparison with existing standalone and other hybrid metaheuristic techniques in terms of clustering validity measures.

Diagnosis of STEMI and Non-STEMI Heart Attack using Nature-inspired Swarm Intelligence and Deep Learning Techniques

Diagnosis of STEMI and Non-STEMI Heart Attack using Nature-inspired Swarm Intelligence and Deep Learning Techniques

Swarm bat algorithm with improved search (SBAIS)

Bat algorithm (BA) is a powerful nature-inspired swarm algorithm which finds applicability to a diverse range of problem domains. Though it is efficient, it suffers from two handicaps: possibility of being trapped in local optima and lost convergence speed as the algorithm progresses. This paper proposes swarm bat algorithm with improved search (SBAIS). SBAIS gains superior exploration capabilities by employing swarming characteristics inspired by shuffled complex evolution (SCE) algorithm. Best bats of the population are kept in a super-swarm, while all other bats are partitioned according to SCE. The super-swarm uses the search mechanism of bat algorithm with improved search to perform refined search around the best solution, which makes sure that the convergence speed of the algorithm is not lost. Every other swarm gets one solution from the super-swarm before starting their evolution process. These swarms evolve using standard bat algorithm, helping the algorithm to escape any possible local optima. SBAIS further keeps a check on the overall diversity of the population. If the diversity drops below a given threshold value, new random solutions are added to the population. Performance of SBAIS is validated by comparing it to BA and fourteen recent variants of bat algorithm over 30 standard benchmark optimization functions, CEC’05 and CEC’14 function sets. Results established the superiority of SBAIS over the compared algorithms.