Recent Swarm Intelligence Algorithm Research Articles

Enhanced Harris hawks optimization with genetic operators for selection chemical descriptors and compounds activities

This paper presents modified versions of a recent swarm intelligence algorithm called Harris hawks optimization (HHO) via incorporating genetic operators (crossover and mutation CM) boosted by two strategies of (opposition-based learning and random opposition-based learning) to provide perfect balance between intensification and diversification and to explore efficiently the search space in order to jump out local optima. Three modified versions of HHO termed as HHOCM, OBLHHOCM and ROBLHHOCM enhance the exploitation ability of solutions and improve the diversity of the population. The core exploratory and exploitative processes of the modified versions are adapted for selecting the most important molecular descriptors ensuring high classification accuracy. The Wilcoxon rank sum test is conducted to assess the performance of the HHOCM and ROBLHHOCM algorithms. Two common datasets of chemical information are used in the evaluation process of HHOCM variants, namely Monoamine Oxidase and QSAR Biodegradation datasets. Experimental results revealed that the three modified algorithms provide competitive and superior performance in terms of finding optimal subset of molecular descriptors and maximizing classification accuracy compared to several well-established swarm intelligence algorithms including the original HHO, grey wolf optimizer, salp swarm algorithm, dragonfly algorithm, ant lion optimizer, grasshopper optimization algorithm and whale optimization algorithm.

Squirrel search algorithm for portfolio optimization

Portfolio Optimization is a standard financial engineering problem. It aims for finding the best allocation of resources for a set of assets. This problem has been studied and different models have been proposed since the classical Mean-Variance model was introduced by Harry Markowitz in 1952 and the later modified version by William Sharpe. The inclusion of real-life constraints to the problem has led to the introduction of the extended Mean-Variance model. However, the successes of nature-inspired algorithms in hard computational optimization problems have encouraged researchers to design and apply these algorithms for a variety of optimization problems. In this paper, we design and adapt a Squirrel Search Algorithm (SSA) for the unconstrained and constrained portfolio optimization problems. SSA is a very recent swarm intelligence algorithm inspired by the dynamic foraging behavior of flying squirrels. The proposed SSA metaheuristic approach is compared with a variety of approaches presented in the literature such as classical single metaheuristics, hybrid metaheuristic approaches and multi-objective optimization approaches for portfolio optimization. Comparative analysis and computational results using different performance indicators show the superiority of the proposed approach for the unconstrained portfolio optimization using both extended Mean-Variance and Sharpe models. For the constrained version of the problem, the proposed approach has also achieved highly competitive results for the different models adopted.

Grasshopper Optimization Algorithm: Theory, Variants, and Applications

Grasshopper Optimization Algorithm (GOA) is a recent swarm intelligence algorithm inspired by the foraging and swarming behavior of grasshoppers in nature. The GOA algorithm has been successfully applied to solve various optimization problems in several domains and demonstrated its merits in the literature. This paper proposes a comprehensive review of GOA based on more than 120 scientific articles published by leading publishers: IEEE, Springer, Elsevier, IET, Hindawi, and others. It provides the GOA variants, including multi-objective and hybrid variants. It also discusses the main applications of GOA in various fields such as scheduling, economic dispatch, feature selection, load frequency control, distributed generation, wind energy system, and other engineering problems. Finally, the paper provides some possible future research directions in this area.

A Reactive Population Approach on the Dolphin Echolocation Algorithm for Solving Cell Manufacturing Systems

In this paper, we integrate the autonomous search paradigm on a swarm intelligence algorithm in order to incorporate the auto-adjust capability on parameter values during the run. We propose an independent procedure that begins to work when it detects a stagnation in a local optimum, and it can be applied to any population-based algorithms. For that, we employ the autonomous search technique which allows solvers to automatically re-configure its solving parameters for enhancing the process when poor performances are detected. This feature is dramatically crucial when swarm intelligence methods are developed and tested. Finding the best parameter values that generate the best results is known as an optimization problem itself. For that, we evaluate the behavior of the population size to autonomously be adapted and controlled during the solving time according to the requirements of the problem. The proposal is testing on the dolphin echolocation algorithm which is a recent swarm intelligence algorithm based on the dolphin feature to navigate underwater and identify prey. As an optimization problem to solve, we test a machine-part cell formation problem which is a widely used technique for improving production flexibility, efficiency, and cost reduction in the manufacturing industry decomposing a manufacturing plant in a set of clusters called cells. The goal is to design a cell layout in such a way that the need for moving parts from one cell to another is minimized. Using statistical non-parametric tests, we demonstrate that the proposed approach efficiently solves 160 well-known cell manufacturing instances outperforming the classic optimization algorithm as well as other approaches reported in the literature, while keeping excellent robustness levels.

Learning-based elephant herding optimization algorithm for solving numerical optimization problems

The elephant herding optimization (EHO) is a recent swarm intelligence algorithm. This algorithm simulates the clan updating and separation behavior of elephants. The EHO method has been successfully deployed in various fields. However, a more reliable implementation of the standard EHO algorithm still requires improving the control and selection of the parameters, convergence speed, and efficiency of the optimal solutions. To cope with these issues, this study presents an improved EHO algorithm terms as IMEHO. The proposed IMEHO method uses a global velocity strategy and a novel learning strategy to update the velocity and position of the individuals. Furthermore, a new separation method is presented to keep the diversity of the population. An elitism strategy is also adopted to ensure that the fittest individuals are retained at the next generation. The influence of the parameters and strategies on the IMEHO algorithm is fully studied. The proposed method is tested on 30 benchmark functions from IEEE CEC 2014. The obtained results are compared with other eight metaheuristic algorithms and evaluated according to Friedman rank test. The results imply the superiority of the IMEHO algorithm to the standard EHO and other existing metaheuristic algorithms.

Social Spider Optimization Algorithm: Theory and its Applications

An extensive variety of optimization problems are solved by swarm intelligence algorithms that are modelled based on the animal or insect behaviour while living in groups. One such recent swarm intelligence algorithm is Social Spider Optimization (SSO). This paper thoroughly reviews and analyses the characteristics of this meta-heuristic algorithm. Since the existing literature of this algorithm is comparatively limited, the paper discusses the research ideas presented in such existing works and classifies the literature on basis of the application areas like image processing, optical flow, electric circuits, neural networks and basic sciences. It also sets a basis for research applications of the algorithm in order to tap the complete potential of the algorithm in other areas to achieve desired results.

Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection

Recently, the number of Internet of Things (IoT) botnet attacks has increased tremendously due to the expansion of online IoT devices which can be easily compromised. Botnets are a common threat that takes advantage of the lack of basic security tools in IoT devices and can perform a series of Distributed Denial of Service (DDoS) attacks. Developing new methods to detect compromised IoT devices is urgent in order to mitigate the negative consequences of these IoT botnets since the existing IoT botnet detection methods still present some issues, such as, relying on labelled data, not being validated with newer botnets, and using very complex machine learning algorithms. Anomaly detection methods are promising for detecting IoT botnet attacks since the amount of available normal data is very large. One of the powerful algorithms that can be used for anomaly detection is One Class Support vector machine (OCSVM). The efficiency of the OCSVM algorithm depends on several factors that greatly affect the classification results such as the subset of features that are used for training OCSVM model, the kernel type, and its hyperparameters. In this paper, a new unsupervised evolutionary IoT botnet detection method is proposed. The main contribution of the proposed method is to detect IoT botnet attacks launched form compromised IoT devices by exploiting the efficiency of a recent swarm intelligence algorithm called Grey Wolf Optimization algorithm (GWO) to optimize the hyperparameters of the OCSVM and at the same time to find the features that best describe the IoT botnet problem. To prove the efficiency of the proposed method, its performance is evaluated using typical anomaly detection evaluation measures over a new version of a real benchmark dataset. The experimental results show that the proposed method outperforms all other algorithms in terms of true positive rate, false positive rate, and G-mean for all IoT device types. Also, it achieves the lowest detection time, while significantly reducing the number of selected features.

Energy-Efficient Routing in WSN: A Centralized Cluster-Based Approach via Grey Wolf Optimizer

Energy efficiency is one of the main challenges in developing Wireless Sensor Networks (WSNs). Since communication has the largest share in energy consumption, efficient routing is an effective solution to this problem. Hierarchical clustering algorithms are a common approach to routing. This technique splits nodes into groups in order to avoid long-range communication which is delegated to the cluster head (CH). In this paper, we present a new clustering algorithm that selects CHs using the grey wolf optimizer (GWO). GWO is a recent swarm intelligence algorithm based on the behavior of grey wolves that shows impressive characteristics and competitive results. To select CHs, the solutions are rated based on the predicted energy consumption and current residual energy of each node. In order to improve energy efficiency, the proposed protocol uses the same clustering in multiple consecutive rounds. This allows the protocol to save the energy that would be required to reform the clustering. We also present a new dual-hop routing algorithm for CHs that are far from the base station and prove that the presented method ensures minimum and most balanced energy consumption while remaining nodes use single-hop communication. The performance of the protocol is evaluated in several different scenarios and it is shown that the proposed protocol improves network lifetime in comparison to a number of recent similar protocols.

A Social Spider Optimization Algorithm for Hybrid Flow Shop Scheduling with Multiprocessor Task

Hybrid flow shop scheduling with multiprocessor task (HFSPMT) is a strongly NP-hard problem that has a wide range of applications in several real-life industrial environments. Due to its essential complexity and practical relevance, HFSPMT has been of an increasing interest for researchers and industrialists in the last two decades. Social spider optimization (SSO) is a recent swarm intelligence algorithm that has been rarely applied on combinatorial optimization problems. In order to make a small step toward having an idea about its effectiveness in solving combinatorial optimization, in this work, a new SSO algorithm is proposed to solve HFSPMT problem with the objective of makespan minimization. The proposed algorithm is experimented on benchmark instances and compared with other existing swarm intelligence algorithms in the literature. The obtained results and the comparisons show that the performance of the proposed algorithm is highly competitive in terms of solution quality.

An accelerated PSO for query expansion in web information retrieval: application to medical dataset

Swarm intelligence algorithms are now among the most widely used soft computing techniques for optimization and computational intelligence. One recent swarm intelligence algorithm that has begun to receive more attention is Accelerated Particle Swarm Optimization (APSO). It is an enhanced version of PSO with global optimization capability, sufficient simplicity and high flexibility. In this paper, we propose the application of the APSO technique to efficiently solve the problem of Query Expansion (QE) in Web Information Retrieval (IR). Unlike prior studies, we introduce a new modelling of QE that aims to find the suitable expanded query from among a set of expanded query candidates. Nevertheless, due to the large number of potential expanded query candidates, it is extremely complex to produce the best one through conventional hard computing methods. Therefore, we propose to consider the problem of QE as a combinatorial optimization problem and address it with APSO. We thoroughly evaluate the proposed APSO for QE using MEDLINE, the world Web’s largest medical library. We first conduct a preliminary experiment to tune the APSO parameters. Then, we compare the results to a recent swarm intelligence algorithm called Firefly Algorithm (FA). We also compare the results with three recently published methods for QE that involved Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and Bat Algorithm (BA). The experimental analysis demonstrates that the proposed APSO for QE is very competitive and yields substantial improvement over the other methods in terms of retrieval effectiveness and computational complexity.

Artificial bee colony algorithm with an adaptive greedy position update strategy

Artificial bee colony (ABC) is a recent swarm intelligence algorithm. There have been some greedy ABC variants developed to enhance the exploitation capability, but greedy variants are usually less reliable and may cause premature convergence, especially without proper control on the greediness degree. In this paper, we propose an adaptive ABC algorithm (AABC), which is characterized by a novel greedy position update strategy and an adaptive control scheme for adjusting the greediness degree. The greedy position update strategy incorporates the information of top t solutions into the search process of the onlooker bees. Such a greedy strategy is beneficial to fast convergence performance. In order to adapt the greediness degree to fit for different optimization scenarios, the proposed adaptive control scheme further adjusts the size of top solutions for selection in each iteration of the algorithm. The adjustment is based on considering the current search tendency of the bees. This way, by combining the greedy position update process and the adaptive control scheme, the convergence performance and the robustness of the algorithm can be improved at the same time. A set of benchmark functions is used to test the proposed AABC algorithm. Experimental results show that the components of AABC can significantly improve the performance of the classic ABC algorithm. Moreover, the AABC performs better than, or at least comparably to, some existing ABC variants as well as other state-of-the-art evolutionary algorithms.

Opposition-Based Adaptive Fireworks Algorithm

A fireworks algorithm (FWA) is a recent swarm intelligence algorithm that is inspired by observing fireworks explosions. An adaptive fireworks algorithm (AFWA) proposes additional adaptive amplitudes to improve the performance of the enhanced fireworks algorithm (EFWA). The purpose of this paper is to add opposition-based learning (OBL) to AFWA with the goal of further boosting performance and achieving global optimization. Twelve benchmark functions are tested in use of an opposition-based adaptive fireworks algorithm (OAFWA). The final results conclude that OAFWA significantly outperformed EFWA and AFWA in terms of solution accuracy. Additionally, OAFWA was compared with a bat algorithm (BA), differential evolution (DE), self-adapting control parameters in differential evolution (jDE), a firefly algorithm (FA), and a standard particle swarm optimization 2011 (SPSO2011) algorithm. The research results indicate that OAFWA ranks the highest of the six algorithms for both solution accuracy and runtime cost.