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Nature’s evolution has shaped intelligent behaviors in creatures like insects and birds, inspiring the field of Swarm Intelligence. Researchers have developed bio-inspired algorithms to address complex optimization problems efficiently. These algorithms strike a balance between computational efficiency and solution optimality, attracting significant attention across domains.

Bio-inspired optimization techniques for feature engineering and its applications are systematically reviewed with chief objective of assessing statistical influence and significance of “Bio-inspired optimization”-based computational models by referring to vast research literature published between year 2015 and 2022.

The Scopus and Web of Science databases were explored for review with focus on parameters such as country-wise publications, keyword occurrences and citations per year. Springer and IEEE emerge as the most creative publishers, with indicative prominent and superior journals, namely, PLoS ONE, Neural Computing and Applications, Lecture Notes in Computer Science and IEEE Transactions. The “National Natural Science Foundation” of China and the “Ministry of Electronics and Information Technology” of India lead in funding projects in this area. China, India and Germany stand out as leaders in publications related to bio-inspired algorithms for feature engineering research.

The review findings integrate various bio-inspired algorithm selection techniques over a diverse spectrum of optimization techniques. Anti colony optimization contributes to decentralized and cooperative search strategies, bee colony optimization (BCO) improves collaborative decision-making, particle swarm optimization leads to exploration-exploitation balance and bio-inspired algorithms offer a range of nature-inspired heuristics.

This paper aims to achieve an optimum flow separation control over the airfoil using a passive flow control method by introducing a bio-inspired nose near the leading edge of the National Advisory Committee for Aeronautics (NACA) 4 and 6 series airfoil. In addition, to find the optimised leading edge nose design for NACA 4 and 6 series airfoils for flow separation control.

Different bio-inspired noses that are inspired by the cetacean species have been analysed for different NACA 4 and 6 series airfoils. Bio-inspired nose with different nose length, nose depth and nose circle diameter have been analysed on airfoils with different thicknesses, camber and camber locations to understand the aerodynamic flow properties such as vortex formation, flow separation, aerodynamic efficiency and moment.

The porpoise nose design that has a leading edge with depth = 2.25% of chord, length = 0.75% of chord and nose diameter = 2% of chord, delays the flow separation and improves the aerodynamic efficiency. Average increments of 5.5% to 6° in the lift values and decrements in parasitic drag (without affecting the pitching moment) for all the NACA 4 and 6 series airfoils were observed irrespective of airfoil geometry such as different thicknesses, camber and camber location.

The two-dimensional computational analysis is done for different NACA 4 and 6 series airfoils at low subsonic speed.

This design improves aerodynamic performance and increases the structural strength of the aircraft wing compared to other conventional high lift devices and flow control devices. This universal leading edge flow control device can be adapted to aircraft wings incorporated with any NACA 4 and 6 series airfoil.

The results would be of significant interest in the fields of aircraft design and wind turbine design, lowering the cost of energy and air travel for social benefits.

Different bio-inspired nose designs that are inspired by the cetacean species have been analysed for NACA 4 and 6 series airfoils and universal optimum nose design (porpoise airfoil) is found for NACA 4 and 6 series airfoils.

This study aims to establish a bio-inspired controller for realizing the bounding gait of a quadruped robot system presented in this paper.

The bio-inspired controller is divided into three levels to mimic the biological patterns of animals. First, the high-level sub-controller is equivalent to the cerebellum, which could plan and control the motion of animals. Second, the effect of the middle-level sub-controller corresponds to the central nervous system. The central pattern generators in the spine generate the stable and cyclic signals as the fundamental rhythm for periodic motion of the leg and spine joints. Third, the low-level sub-controller is equal to the end effector, which adopts the simple proportional-derivative (PD) control to realize the specific motion trajectory of the legs and spine.

Combined with the stability criterion presented previously and the delayed feedback control method, the bounding gait of the cheetah virtual prototype could be actuated and stabilized by the bio-inspired controller. Moreover, the bio-inspired controller is applied to realize the bounding gait of an SQBot, which is a quadruped robot with a spine joint. Meanwhile, the validity and practicability of the bio-inspired controller for the control of quadruped robot have been verified against different forward velocities.

The bio-inspired controller and bionic quadruped robot system are instructive for the designing and actuating of the real quadruped robot.

The aim of this research paper is to examine the bio‐inspired optimization algorithms, namely, genetic algorithm (GA), particle swarm optimization (PSO) and bacterial foraging optimization (BFO) algorithm with adaptive chemotactic step for determining the steady‐state equivalent circuit parameters of the three‐phase induction motor using a set of manufacturer data.

The induction motor parameter determination issue is devised as a nonlinear constrained optimization problem. The nonlinear equations of various quantities (torque, current and power factor) are derived in terms of equivalent circuit parameters from a single and a double‐cage model, and then, equates to the corresponding manufacturer data. These equations are solved by the bio‐inspired algorithms. Using the squared error between the determined and the manufacturer data as the objective function, the parameter determination problem is transferred into an optimization process where the model parameters are determined that minimize the defined objective function. The objective function is iteratively minimized using GA, PSO and BFO techniques. In order to balance the exploration and exploitation searches of the BFO algorithm, an adaptive chemotactic step is utilized.

Comparisons of the results of GA, PSO, BFO and IEEE Std. 112‐F (using no‐load, locked‐rotor and stator resistance tests) methods for two sample motors are presented. Results show the superiority of the bio‐inspired optimization algorithms over the classical one. Besides, BFO‐based parameter determination method is observed to obtain better quality solutions quickly than GA and PSO methods.

The parameters obtained by the proposed approaches can be used in analyzing the stalling and/or reacceleration process of a loaded motor following a fault or during voltage sag condition as well as in system‐level studies.

The most significant contribution of the research is the potential to determine the equivalent circuit parameters of induction motor only from its manufacturer data without conducting any lab tests on the motor. The bio‐inspired optimization based parameter determination approaches are faster and less intrusive than the IEEE Std. 112‐F method.

The purpose of this paper is to achieve an optimum flow separation control over the airfoil using passive flow control method by introducing bio-inspired nose near the leading edge of the NACA 2412 airfoil.

Two distinguished methods have been implemented on the leading edge of the airfoil: forward facing step, which induces multiple accelerations at low angle of attack, and cavity/backward facing step, which creates recirculating region (axial vortices) at high angle of attack.

The porpoise airfoil (optimum bio-inspired nose airfoil) delays the flow separation and improves the aerodynamic efficiency by increasing the lift and decreasing the parasitic drag. The maximum increase in aerodynamic efficiency is 22.4 per cent, with an average increase of 8.6 per cent at all angles of attack.

The computational analysis has been done for NACA 2412 airfoil at low subsonic speed.

This design improves the aerodynamic performance and increases structural strength of the aircraft wing compared to other conventional high-lift devices and flow-control devices.

Different bio-inspired nose designs which are inspired by the cetacean species have been analysed for NACA 2412 airfoil, and optimum nose design (porpoise airfoil) has been found.

This paper aims to present a state-of-the-art review in various fields of interest, leading to a new concept of bio-inspired control of small aircraft. The main goal is to improve controllability and safety in flying at low speeds.

The review part of the paper gives an overview of artificial and natural flow sensors and haptic feedback actuators and applications. This background leads to a discussion part where the topics are synthesized and the trend in control of small aircraft is estimated.

The gap in recent aircraft control is identified in the pilot–aircraft interaction. A pilot’s sensory load is discussed and several recommendations for improved control system architecture are laid out in the paper.

The paper points out an opportunity for a following research of suggested bio-inspired aircraft control. The control is based on the artificial feeling of aerodynamic forces acting on a wing by means of haptic feedback.

The paper merges two research fields – aircraft control and human–machine interaction. This combination reveals new possibilities of aircraft control.

This paper aims to present an approach for a bio‐inspired decentralization topology control mechanism, called force‐based genetic algorithm (FGA), where a genetic algorithm (GA) is run by each holonomic autonomous vehicle (HAV) in a mobile ad hoc network (MANET) as software agent to achieve a uniform spread of HAVs and to provide a fully connected network over an unknown geographical terrain. An HAV runs its own FGA to decide its next movement direction and speed based on local neighborhood information, such as obstacles and the number of neighbors, without a centralized control unit or global knowledge.

The objective function used in FGA is inspired by the equilibrium of the molecules in physics where each molecule tries to be in the balanced position to spend minimum energy to maintain its position. In this approach, a virtual force is assumed to be applied by the neighboring HAVs to a given HAV. At equilibrium, the aggregate virtual force applied to an HAV by its neighbors should sum up to zero. If the aggregate virtual force is not zero, it is used as a fitness value for the HAV. The value of this virtual force depends on the number of neighbors within the communication range of R_com and the distance among them. Each chromosome in our GA‐based framework is composed of speed and movement direction. The FGA is independently run by each HAV as a topology control mechanism and only utilizes information from neighbors and local terrain to make movement and speed decisions to converge towards a uniform distribution of HAVs. The authors developed an analytical model, simulation software and several testbeds to study the convergence properties of the FGA.

The paper finds that coverage‐centric, bio‐inspired, mobile node deployment algorithm ensures effective sensing coverage for each mobile node after initial deployment. The FGA is also an energy‐aware self‐organization framework since it reduces energy consumption by eliminating unnecessary excessive movements. Fault‐tolerance is another important feature of the GA‐based approach since the FGA is resilient to losses and malfunctions of HAVs. Furthermore, the analytical results show that the authors' bio‐inspired approach is effective in terms of convergence speed and area coverage uniformity. As seen from the experimental results, the FGA delivers promising results for uniform autonomous mobile node distribution over an unknown geographical terrain.

The proposed decentralized and bio‐inspired approach for autonomous mobile nodes can be used as a real‐time topology control mechanism for commercial and military applications since it adapts to local environment rapidly but does not require global network knowledge.

This paper aims to present a phenomenological model to investigate the underlying mechanism and predict the bio-inspired performance under different thermo-temporal conditions.

Flory-Rehner free-energy functions are applied to quantitatively identify the driving forces in the viscously bio-inspired response of a dynamic polymer network. Furthermore, the permeation transition equation is adopted to couple water gradient and water sorption/desorption into the free-energy function.

The results show that the influence of potential energy on deformation can be related to a stretching ratio that uniquely determines water sorption/desorption, locomotion frequency and contractile stress. Finally, by means of combining the free-energy function and Arrhenius equation, a phenomenological thermo-temporal model is developed and verified by the experimental results.

This study focuses on exploring the theoretical mechanism and significantly enhances understanding of relevant experimental features reported previously.

The outcome of this study will provide a powerful phenomenological and quantitative tool for study on shape memory effect in bio-inspired polymers.

The purpose of this paper is to apply a hybrid neuro-fuzzy paradigm called self-organizing neuro-fuzzy multilayered classifier (SONeFMUC) to classify the operating faults of a hydraulic system. The main motivation behind the use of SONeFMUC is to attest the capabilities of neuro-fuzzy classifier for handling the difficulties associated with fault diagnosis of hydraulic circuits.

In the proposed methodology, first, the neuro-fuzzy nodes at each layer of the SONeFMUC are trained separately using two well-known bio-inspired algorithms, i.e. a semi deterministic method with random walks called co-variance matrix adaptation evolutionary strategy (CMA-ES) and a swarm-based explorer with adaptive fuzzified parameters (SBEAFP). Thereafter, a revised version of the group method data handling (GMDH) policy that uses the Darwinian concepts such as truncation selection and elitism is engaged to connect the nodes of different layers in an effective manner.

Based on comparative numerical experiments, the authors conclude that integration of neuro-fuzzy method and bio-inspired supervisor results in a really powerful classification tool beneficial for uncertain environments. It is proved that the method outperforms some well-known classifiers such as support vector machine (SVM) and particle swarm optimization-based SVM (PSO-SVM). Besides, it is indicated that an efficient bio-inspired method can effectively adjust the constructive parameters of the multi-layered neuro-fuzzy classifier. For the case, it is observed that designing a fuzzy controller for PSO predisposes it to effectively balance the exploration/exploitation capabilities, and consequently optimize the structure of SONeFMUC.

The originality of the paper can be considered from both numerical and practical points of view. The signals obtained through the data acquisition possess six different features in order for the hydraulic system to undergo four types of faults, i.e. cylinder fault, pump fault, valve leakage fault and rupture of the piping system. Besides, to elaborate on the authenticity and efficacy of the proposed method, its performance is compared with well-known rival techniques.

The purpose of this paper is to introduce bio-inspired FeN₄-S-C black nano-electrocatalyst for the oxygen reduction reaction (ORR) in an alkaline medium. The FeN₄-S-C derived without pyrolysis of precursors in high temperature is recognized as a new electrocatalyst for the ORR in an alkaline electrolyte. For the proper design of bio-inspired nano-electrocatalyst for the ORR performance, chlorinated iron (II) phthalocyanine nanoparticles were used as templates for achieving the active sites in aqueous KOH by rotating disk electrode methods. The most active FeN₄-S-C catalyst exhibited a remarkable ORR activity in the alkaline medium. The objectives of this paper are to investigate the possibility of nanoscale particles size (Ëœ5nm) of electrocatalyst, to achieve four-electron transfer mechanism and to exhibit much superior catalytic stability in measurements. This paper will shed light on bio-inspired FeN₄-S-C materials for the ORR catalysis in alkaline fuel cells.

The paper presents a new bio-inspired nano-electrocatalyst for the ORR, which has activity nearby platinum/carbon electrocatalyst. Chlorinated iron phthalocyanine nanoparticles have been used as FeN₄ template, which is the key point for the ORR. Bio-inspired nano-electrocatalyst has been fabricated using chlorinated iron phthalocyanine, sodium sulphide and carbon black.

The particles’ size was 5 nm and electron transfer number was 4.

The catalyst that is used in this method should be weighed carefully. In addition, the solvent should be a saturated solution of NaCl in water.

The method provides a simple and practical solution to improving the synthesis of iron-based catalyst for ORR.

The method for the synthesis of bio-inspired electrocatalyst was novel and can find numerous applications in industries, especially as ORR non-precious metal catalyst.