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Image contrast enhancement (CE) is a frequent image enhancement requirement in diverse applications. Histogram equalization (HE), in its conventional and different further improved ways, is a popular technique to enhance the image contrast. The conventional as well as many of the later versions of HE algorithms often cause loss of original image characteristics particularly brightness distribution of original image that results artificial appearance and feature loss in the enhanced image. Discrete Cosine Transform (DCT) coefficient mapping is one of the recent methods to minimize such problems while enhancing the image contrast. Tuning of DCT parameters plays a crucial role towards avoiding the saturations of pixel values. Optimization can be a possible solution to address this problem and generate contrast enhanced image preserving the desired original image characteristics. Biological behavior-inspired optimization techniques have shown remarkable betterment over conventional optimization techniques in different complex engineering problems. Gray wolf optimization (GWO) is a comparatively new algorithm in this domain that has shown promising potential. The objective function has been formulated using different parameters to retain original image characteristics. The objective evaluation against CEF, PCQI, FSIM, BRISQUE and NIQE with test images from three standard databases, namely, SIPI, TID and CSIQ shows that the presented method can result in values up to 1.4, 1.4, 0.94, 19 and 4.18, respectively, for the stated metrics which are competitive to the reported conventional and improved techniques. This paper can be considered a first-time application of GWO towards DCT-based image CE. [ABSTRACT FROM AUTHOR]

Nature-inspired metaheuristic approaches draw their core idea from biological evolution in order to create new and powerful competing algorithms. Such algorithms can be divided into evolution-based and swarm-based algorithms. This paper proposed a new nature-inspired optimizer called the Greylag Goose Optimization (GGO) algorithm. The proposed algorithm (GGO) belongs to the class of swarm-based algorithms and is inspired by the Greylag Goose. Geese are excellent flyers and during their seasonal migrations, they fly in a group and can cover thousands of kilometers in a single flight. While flying, a group of geese forms themselves as a "V" configuration. In this way, the geese in the front can minimize the air resistance of the ones in the back. This allows the geese to fly around 70% farther as a group than they could individually. The GGO algorithm is first validated by being applied to nineteen datasets retrieved from the UCI Machine Learning Repository. Each dataset contains a varied amount of characteristics, instances, and classes that are used to choose features. After that, it is put to use in the process of solving a number of engineering benchmark functions and case studies. Several case studies are solved using the proposed algorithm too, including the pressure vessel design and the tension/compression spring design. The findings demonstrate that the GGO method outperforms numerous other comparative optimization algorithms and delivers superior accuracy compared to other algorithms. The results of the statistical analysis tests, such as Wilcoxon's rank-sum and one-way analysis of variance (ANOVA), demonstrate that the GGO algorithm achieves superior results. • Greylag Goose Optimization (GGO) algorithm inspired by nature is proposed. • A binary version of the GGO algorithm is designed for feature selection. • The GGO algorithm is tested on 19 datasets from UCI Machine Learning Repository. • The GGO algorithm is applied to engineering problems and benchmark functions. • The statistical significance of the GGO algorithm has been evaluated. [ABSTRACT FROM AUTHOR]

This work addresses the deployment problem in Wireless Sensor Networks (WSNs) by hybridizing two metaheuristics, namely the Bees Algorithm (BA) and the Grasshopper Optimization Algorithm (GOA). The BA is an optimization algorithm that demonstrated promising results in solving many engineering problems. However, the local search process of BA lacks efficient exploitation due to the random assignment of search agents inside the neighborhoods, which weakens the algorithm's accuracy and results in slow convergence especially when solving higher dimension problems. To alleviate this shortcoming, this paper proposes a hybrid algorithm that utilizes the strength of the GOA to enhance the exploitation phase of the BA. To prove the effectiveness of the proposed algorithm, it is applied for WSNs deployment optimization with various deployment settings. Results demonstrate that the proposed hybrid algorithm can optimize the deployment of WSN and outperforms the state-of-the-art algorithms in terms of coverage, overlapping area, average moving distance, and energy consumption. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) comprise a large number of tiny sensing nodes, which are battery-powered with limited energy. An energy-efficient routing protocol is of utmost importance to prolong the network lifetime. Clustering is the most common technique to balance energy consumption among all nodes, while minimizing traffic and overhead during the data transmission phases. In this paper, a Multi-Objective nature-inspired algorithm based on Shuffled frog-leaping algorithm and Firefly Algorithm (named MOSFA) as an adaptive application-specific clustering-based multi-hop routing protocol for WSNs is proposed. MOSFA's multi-objective function regards different criteria (e.g., inter- and intra-cluster distances, the residual energy of nodes, distances from the sink, overlap, and load of clusters) to select appropriate cluster heads at each round. Moreover, another multi-objective function is proposed to select the forwarder nodes in the routing phase. The controllable parameters of MOSFA in both clustering and multi-hop phases can be adaptively tuned to achieve the best performance based on the network requirements according to the specific application. Simulation results demonstrate average lifetime improvements of 182%, 68%, 30%, and 28% when compared with LEACH, ERA, SIF, and FSFLA, respectively, in different network scenarios. [ABSTRACT FROM AUTHOR]

Data mining integrates statistical analysis, machine learning and database technology to extract hidden patterns and relationships from data. The presence of irrelevant, redundant and inconsistent attributes in the data ushers poor classification accuracy. In this paper, a novel bio-inspired heuristic swarm optimization algorithm for feature selection, namely Constructive Lazy Wolf Search Algorithm is proposed based on the backbone of the Wolf Search Algorithm. It is based on the behavior of the real wolves, which search for their food and consequently survive the attacks of the threats by avoiding them. Based on the study conducted on the behavior of wolves two natural factors, namely laziness and health are introduced for attaining highest efficiency. Restricting and controlling the wolves' behavior by allowing only healthy and constructive lazy wolves to take part in the search reduces the search time and complexity required to search for the best fitness. The proposed algorithm is then applied on a prisoner dataset for crime propensity prediction along with a few benchmark datasets to prove the stability in the improved performance compared with other bio-inspired optimization algorithms. The accuracy achieved by fine-tuning the proposed algorithm was 98.19% providing accurate crime prevention. [ABSTRACT FROM AUTHOR]

Massively complex systems, such as social networks (Camacho, Panizo-LLedot, Bello-Orgaz, Gonzalez-Pardo, & Cambria, 2020; Lara-Cabrera et al., 2017), renewable energy problems (Twidell & Weir, 2015), or Internet-of-Things problems (Lin et al., 2017), generate massive amounts of data. This special issue welcomed submissions of original papers introducing research results on all the aspects covering the application of CI algorithms to massively complex systems, ranging from concepts and theoretical developments to advanced technologies and innovative applications. GRASP algorithm is also used in the paper by Pérez-Peló, Sánchez-Oro, & Duarte (2020), where the authors tackle the I i -separator problem. [Extracted from the article]

This paper introduces a novel population-based bio-inspired meta-heuristic optimization algorithm, called Blood Coagulation Algorithm (BCA). BCA derives inspiration from the process of blood coagulation in the human body. The underlying concepts and ideas behind the proposed algorithm are the cooperative behavior of thrombocytes and their intelligent strategy of clot formation. These behaviors are modeled and utilized to underscore intensification and diversification in a given search space. A comparison with various state-of-the-art meta-heuristic algorithms over a test suite of 23 renowned benchmark functions reflects the efficiency of BCA. An extensive investigation is conducted to analyze the performance, convergence behavior and computational complexity of BCA. The comparative study and statistical test analysis demonstrate that BCA offers very competitive and statistically significant results compared to other eminent meta-heuristic algorithms. Experimental results also show the consistent performance of BCA in high dimensional search spaces. Furthermore, we demonstrate the applicability of BCA on real-world applications by solving several real-life engineering problems. [ABSTRACT FROM AUTHOR]

Bio-inspired computing is an engaging area of artificial intelligence which studies how natural phenomena provide a rich source of inspiration in the design of smart procedures able to become powerful algorithms. Many of these procedures have been successfully used in classification, prediction, and optimization problems. Swarm intelligence methods are a kind of bio-inspired algorithm that have been shown to be impressive optimization solvers for a long time. However, for these algorithms to reach their maximum performance, the proper setting of the initial parameters by an expert user is required. This task is extremely comprehensive and it must be done in a previous phase of the search process. Different online methods have been developed to support swarm intelligence techniques, however, this issue remains an open challenge. In this paper, we propose a hybrid approach that allows adjusting the parameters based on a state deducted by the swarm intelligence algorithm. The state deduction is determined by the classification of a chain of observations using the hidden Markov model. The results show that our proposal exhibits good performance compared to the original version. [ABSTRACT FROM AUTHOR]

This paper introduces a bio-inspired metaheuristic optimization algorithm named Tunicate Swarm Algorithm (TSA). The proposed algorithm imitates jet propulsion and swarm behaviors of tunicates during the navigation and foraging process. The performance of TSA is evaluated on seventy-four benchmark test problems employing sensitivity, convergence and scalability analysis along with ANOVA test. The efficacy of this algorithm is further compared with several well-regarded metaheuristic approaches based on the generated optimal solutions. In addition, we also executed the proposed algorithm on six constrained and one unconstrained engineering design problems to further verify its robustness. The simulation results demonstrate that TSA generates better optimal solutions in comparison to other competitive algorithms and is capable of solving real case studies having unknown search spaces. Note that the source codes of the proposed TSA algorithm are available at [ABSTRACT FROM AUTHOR]