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• The different variants of firefly algorithm are classified and analyzed to summarize the latest research and developments related to nature-inspired computing in the most general sense. • This review has proved that firefly algorithms can be practically applied within every problem domain. Moreover, this review has presented to researchers new technological trends and design challenges they have to cope with, while designing systems using this algorithm. Firefly algorithm (FA) is one of the popular algorithms of Swarm Intelligence domain. The literature has expanded significantly in the past few years. This paper provides a timely review of the FA and its new variants. The different variants of FA are classified and analyzed. The applications and case studies in the rapidly evolving domain are also reviewed and summarized in detail here. The problems to be further solved and future research directions of firefly algorithms will be given at the end of this paper. [ABSTRACT FROM AUTHOR]

• This paper presents an enhanced salp swarm algorithm for solving global optimization problems. • The random replacement strategy is used to accelerate the convergence speed of the basic salp swarm algorithm. • The double adaptive weighting strategy enhances the algorithm's global exploration and local exploitation capabilities. • The optimization performance of the improved algorithm is evaluated on basic test functions and practical problems. The salp swarm algorithm is a newly population-based search algorithm. Because the original salp swarm algorithm has low search efficiency and is easy to fall into local optimum, in this paper, we propose an enhanced salp swarm algorithm, which combines two strategies with the original salp swarm algorithm. One is the random replacement strategy, which can replace the current position with the optimal solution position with a certain probability of speeding up the convergence rate. The other tactic is double adaptive weight, which can expand the search scope throughout the early stages and enhance exploitation capability in the later stages. With the cooperation and guidance of the two mechanisms, the algorithm's convergence speed is accelerated, and the exploitation capacity is meritoriously increased. The proposed method's performance is compared with three mainstream meta-heuristics and four advanced algorithms on four necessary test cases. The extensive analysis and recorded results indicate that the proposed method outperforms these algorithms in terms of the accuracy of the solution and convergence speed. Finally, we apply the developed method to four well-known engineering design problems (welded beam design problem; cantilever beam design; I-beam design; and multiple disk clutch brake) to validate the algorithm's effectiveness for some constrained challenge. The results show that our algorithm has significant advantages in solving practical problems with constraints and unknown search spaces. [ABSTRACT FROM AUTHOR]

Accurate estimation of the model parameters of a fuel cell is extremely important to study its system behavior. This paper presents a parameter identification method based on improved artificial bee colony algorithm (IABC). Inspired by the cross operation of genetic algorithm and the cross and mutation operation of differential evolution algorithm, this study improves the position update formula of ABC in the parameter optimization process, thus the convergence accuracy and speed of IABC algorithm are improved. The algorithm was used to identify 3 typical standard test cases, namely 250W FC, BCS 500W and NedStack PS6 stack, and the minimum sum of squares (SSE) error between PEMFC measured voltage data and estimated voltage data was defined as the objective function. The SSE values of the three PEMFCs reached 0.29686, 0.011718 and 2.9848, respectively. The model I/V polarization curve estimated by IABC algorithm has a high fitting degree with the experimental measured curve, which indicates the accuracy of IABC algorithm. To verify the superiority of the proposed IABC algorithm, the identification results obtained by some optimization algorithms are compared and ANOVA (analysis of variance) is performed. Results show that the IABC algorithm has faster convergence speed and better robustness. • In this paper, a PEMFC parameter identification method based on improved artificial bee colony algorithm (IABC) is proposed to improve the original artificial bee colony. • The improved artificial bee colony algorithm further improves the accuracy of parameter identification and accelerates the convergence speed of the algorithm. • Three typical PEMFC stacks are tested and the results are compared with other algorithms to verify the advantages of IABC. [ABSTRACT FROM AUTHOR]

Being sensitive to initial position and noise is the key problem that must be solved in point cloud registration. To improve the overall performance of registration methods, a point cloud registration method based on the Dark Forest Algorithm is proposed in this paper. The introduction of swarm intelligence enhances the adaptability of the registration method to the initial position and improves the robustness of the point cloud registration system. Furthermore, the application of the Dark Forest Algorithm with four capability enhancement strategies: the hierarchical elite, civilization self-decision, suspicion chain, and technology explosion has improved the comprehensive competitiveness in point cloud registration. The mentioned strategies have brought a great positive impact on the registration methods in solving the far initial position, breaking away from the local optimal solution, and weakening the noise interference. The paper aims to provide the Dark Forest Algorithm and the point cloud registration method based on it, which are elaborated on in detail and compared to other existing algorithms to prove the comprehensive performance, respectively. Then, the point cloud registration experiment for coal-wall point clouds was carried out and the results indicate that the proposed registration algorithm is better than other algorithms for standard point cloud dataset and coal-wall point cloud, which demonstrates that the proposed registration method has strong potential competitiveness. • Proposed a point cloud registration based on a novel meta-heuristic algorithm. • Dark Forest Algorithm is proposed with the higher accuracy and stronger adaptability. • Cloud registration based on the DFA demonstrates potential competitiveness. [ABSTRACT FROM AUTHOR]

Extracting relevant information from a large amount of data is a challenging task. Automatic text summarization is a potential solution for obtaining this information. In this paper, a nature inspired swarm intelligence-based algorithm viz. firefly algorithm for multi-document text summarization is proposed. A new fitness function consisting of three features viz. topic relation factor, cohesion factor and readability factor is utilized. The experiments are performed on datasets from Document Understanding Conference i.e. DUC-2002, DUC-2003 and DUC-2004. The performance of the algorithm has been evaluated using ROUGE score. The performance of the proposed algorithm is compared with some other nature inspired ones such as particle swarm optimization (PSO) and genetic algorithm (GA). The performance of the proposed algorithm outperforms the other adopted ones. [ABSTRACT FROM AUTHOR]

Decision tree induction is a simple, however powerful learning and classification tool to discover knowledge from the database. The volume of data in databases is growing to quite large sizes, both in the number of attributes and instances. Some important limitations of decision trees are instability, local decisions, and overfitting for this extensive data. The simple, effective and non-convergence nature of the African Buffalo Optimization (ABO) algorithm makes it suitable to solve complex optimization problems. In this paper, we propose the African Buffalo Optimized Decision Tree (ABODT) algorithm to create globally optimized decision trees using the intelligent and collective behaviour of African Buffalos. The modified African Buffalo optimization algorithm is used to create efficient and optimal decision trees. To evaluate the efficiency of the proposed African Buffalo Optimized Decision Tree algorithm, experiments are performed on 15 standard UCI learning repository datasets that are of various sizes and domains. Results show that the African Buffalo Optimized Decision Tree algorithm globally optimizes decision trees, increases accuracy and reduces the size of a decision tree. These optimized trees are stable and efficient than conventional decision trees. [ABSTRACT FROM AUTHOR]

Aiming at the problem that Ant Colony Optimization (ACO) is subject primarily to the parameters, we propose a hybrid algorithm SOA-ACO-2Opt to optimize the ACO parameter combination through Seagull Optimization Algorithm (SOA) to strengthen ACO's search capability. To obtain a uniform initial distribution of the ACO parameter combination, we incorporated the Kent Chaos Map (KCM) to randomly initialize the seagull's position, reducing the tendency of SOA to fall into the local optimum. To avoid slow calculation speed and premature convergence of ACO, we improved the adaptive multi-population mechanism to reduce repeated redundant calculations and used the ϵ − g r e e d y and default strategy, respectively, to update the ants' position. 2Opt is applied to find shorter paths in each iteration. In addition, when AUV navigates on the planned path, it may encounter obstacles. Therefore, this paper proposes an autonomous obstacle avoidance algorithm based on forward-looking sonar to ensure safety during tasks. SOA-ACO-2Opt is verified against twelve different problems extracted from TSPLIB and compared with some state-of-the-art algorithms. Furthermore, sea trials were carried out for several representative marine engineering applications of TSP and obstacle avoidance. Experimental results show that this work can significantly improve AUV's work efficiency and intelligence and protect the AUV's safety. [Display omitted] • A hybrid algorithm SOA-ACO-2Opt is proposed to solve TSP. • A multi-population mechanism is used to reduce redundant computation and avoid early convergence. • The desired-path-based avoidance strategy is efficient and of low complexity. • The proposed algorithm is practical and efficient for AUV engineering applications. [ABSTRACT FROM AUTHOR]

In recent years, there have been notable advancements in the theoretical research of existing meta-heuristic algorithms and in the development of novel algorithms. However, the research on the definition and construction methods of the so-called "mutation" operator, a concept widely used in existing meta-heuristic algorithms, remains notably insufficient. In addition, how to directly construct a meta-heuristic algorithm under the guidance of stability and invariance analysis continues to pose a challenge that has yet to resolve. This paper carefully investigates and rigorously defines the concepts of "mutation operator" and "mutation-driven meta-heuristic algorithm" as found in the literature. According to these definitions, a novel meta-heuristic algorithm, called Multi-direction guided Mutation-driven Stable swarm Intelligence algorithm with Translation and Rotation invariance (M2SITRI) is proposed, and the corresponding mutation operator is carefully designed based on the analysis of translation invariance, rotation invariance, order-1 stability, and order-2 stability. The stability and invariance properties of the M2SITRI algorithm are validated through numerical simulations, and its superior solving performance is demonstrated by benchmarking it against several other well-known existing meta-heuristic algorithms across a variety of test problems. • Mutation operator and mutation-driven meta-heuristic algorithm concepts are defined. • A novel translation and rotationally invariant meta-heuristic algorithm termed M2SITRI is proposed. • The invariance and stability properties and the excellent solving performance of M2SITRI algorithm are verified. [ABSTRACT FROM AUTHOR]

Artificial rabbits optimization (ARO) is a swarm intelligence-based algorithm inspired by the survival strategies of rabbits. Although ARO has a good convergence rate, it is prone to get stuck in the local optima and converge prematurely. To overcome this, the present paper redesigns the exploration operator of the ARO algorithm with the roulette fitness-distance balance (RFDB) and dynamic fitness-distance balance (d FDB) strategies. In this context, three different versions of the fitness-distance balance-based artificial rabbits optimization (FDBARO) algorithm are developed. The performance of the original ARO and FDBARO versions (FDBARO-1, FDBARO-2, and FDBARO-3) are evaluated on CEC 2017 and CEC 2020 benchmark functions. The obtained results are analyzed with the Wilcoxon and Friedman statistical tests. Statistical and convergence analysis results showed that the FDBARO-3 algorithm designed with the d FDB selection method can explore the search space more successfully compared to other algorithms. This version was named the dynamic FDBARO (d FDBARO) algorithm. Moreover, the practicability of the proposed d FDBARO is highlighted by the solution of the optimal power flow (OPF) problem formulated with renewable energy sources (RESs) and flexible alternating current transmission system (FACTS) devices considering fixed and uncertain load demands. Experimental results showed that the proposed d FDBARO is a competitive algorithm for solving global optimization and constrained OPF problems. The source code of the d FDBARO algorithm is available at https://www.mathworks.com/matlabcentral/fileexchange/154845-dfdbaro-an-enhanced-metaheuristic-algorithm. [ABSTRACT FROM AUTHOR]

Many real-world problems can be abstracted as multimodal global optimization, which is one of the main challenges for optimization algorithms due to its complexity. The fireworks algorithm (FWA) is a swarm intelligence optimization algorithm that has been widely studied and applied by virtue of the synergistic property among fireworks. Current FWA variants have poor exploitation capability to handle some locally complex multimodal functions, which greatly limits the application of the FWA to practical problems. To solve the above problems, in this paper, we propose the multi-guiding spark fireworks algorithm (MGFWA) to solve multimodal functions by enhancing FWA exploitation capabilities. Three different strategies which are boosted guiding vector, multi-guiding sparks, and population-based random mapping are designed to boost the guiding vector, enrich the guiding spark diversity, and fix the mapping function, separately. The validity and parameter setting of MGFWA are theoretically analyzed. Experimentally, the results on the CEC2013 and CEC2017 single objective optimization benchmarks illustrate the remarkable performance of the MGFWA compared to other typical optimization algorithms and FWA variants. Moreover, the ablation study shows each of the three parts plays an important role in the algorithm and the efficiency experiment shows the MGFWA can improve the efficiency of guiding sparks by 10%. We believe that the MGFWA can be considered the SOTA variant of the FWA. • Utilizing boosted guiding vector, multi-guiding sparks, and population-based random mapping to enhance the exploitation capability of the fireworks algorithm. • State-of-the-art performance among fireworks algorithm variants. • Superior performance on multimodal global optimization among several typical swarm intelligence optimization algorithms and evolutionary algorithms. • Extensive experiments (about 60 test functions) and theoretical analyses to demonstrate the effectiveness. [ABSTRACT FROM AUTHOR]

The jellyfish search optimizer (JSO) is one of the newest swarm intelligence algorithms which has been widely used to solve different real-world optimization problems. However, its most challenging task is to regulate the exploration and exploitation search to avoid problems in harmonic convergence or be trapped into local optima. In this paper, we propose a new variant of JSO named OJSO, based on orthogonal learning with the aim to improve the capability of global searching of the original algorithm. The orthogonal learning is a strategy for discovering more useful information from two recent solution vectors by predicting the best combination using limited trials instead of exhaustive trials via an orthogonal experimental design. To evaluate the effectiveness of our approach, 23 benchmark functions are used. The evaluation process leads us to conclude that the proposed algorithm strongly outperforms the original algorithm in all aspects except the execution time. [ABSTRACT FROM AUTHOR]

In this paper, we propose an intelligent scheme and design a spatial information sampling algorithm (SIS) to achieve a balance between exploitation and exploration more efficiently. In SIS, by creatively using a chaotic map, we divide the population of the algorithm into external and internal parts and activate the corresponding exploitation or exploration operations according to the location where the optimal individuals appear in the population. The intelligent scheme makes full use of the spatial information in the solution space where the algorithm's population is located. It also gives the algorithm the authority to start exploitation or exploration operations autonomously based on the information in the space, which increases the flexibility of the algorithm in complex optimization problems. This novel scheme not only further promotes the intelligence of the algorithm in a simple architecture but also creates a new way of balancing exploitation and exploration in the optimization process. We test the SIS on IEEE CEC2017, IEEE CEC2011, an artificial neural model training problem, and a position optimization problem of wave energy converters. When compared to other state-of-the-art meta-heuristics, the results of the Wilcoxon signed-rank test, Wilcoxon rank-sum test, and Friedman test revealed that SIS possesses superior solutions in terms of global optimality, avoidance of local minima, and solution quality. The source code of SIS is released at https://toyamaailab.github.io/. • This paper proposes a new scheme to balance the exploitation and exploration. • The new scheme enables SIS to independently perceive, analyze, and take action. • SIS uses chaotic map to construct a population to realize an intelligent scheme. • SIS has high flexibility in dealing with different optimization problems. • The simulation results show that SIS is superior to the state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

• A novel wireless multi-channel MMG acquisition system is designed. • Four swarm intelligence algorithms (GA, PSO, WOA, SSA) and three methods (ReliefF, PCA, SFS) for feature selection. • By adding the weight factor to improve the three algorithms (IPSO, IWOA, ISSA), better classification results were achieved. • The classification accuracy of eight types of lower limb movements is over 90%. Mechanomyography (MMG) is a kind of biomedical signal with great research value. This paper designed a novel wireless MMG signal acquisition system composed of modular nodes and core board. The modular nodes were worn on different muscle of the lower limbs, collecting movement data of the corresponding part, and transmitted it to the core board wirelessly. The core board was connected to the computer through the USB to achieve the wireless collection and real-time display of MMG signals. In order to improve the real-time performance, this paper adopted four swarm intelligence algorithms (GA, PSO, WOA, SSA) and three improved algorithms (IPSO, IWOA, ISSA) for feature selection to reduce the redundancy of information. In this study, the different effects of different feature selection methods on the recognition of eight types of lower limb movements based on MMG signals were discussed by comparing the classification accuracy, number of iterations and calculation time. The results show that swarm intelligence algorithms have merits in this type of feature selection problem, and GA has the most obvious effect in improving classification accuracy, but it requires more iterations and calculation time, while the classification accuracy of IPSO is close to that of GA, and it has advantages in time consumption. [ABSTRACT FROM AUTHOR]

Nowadays, more and more optimization techniques are used to deal with complex engineering optimization problems. Firefly algorithm (FA) inspired by the flash communication between fireflies, has been proven to be competitive with other swarm intelligence algorithms and has been widely applied to solve complex engineering optimization problems. However, FA has some defects in dealing with complex engineering optimization problems, such as the exploration and exploitation cannot be well balanced. Therefore, in order to achieve effective performance, the different characteristics of search strategies can be applied at different stages of the search process to achieve a balance between exploration and exploitation. In this paper, a multi-strategy firefly algorithm with selective ensemble (MSEFA) is proposed. In MSEFA, the algorithm has three novel search strategies with different characteristics in the strategy pool. In addition, an idea of selective ensemble is adopted to design a priority roulette selection method. The method can select suitable search strategies in different search stages and coordinate the balance of strategies so that better results can be obtained. Furthermore, a parameter adaptive transformation mechanism is designed to control the decreasing rate of step size α. To verify the effectiveness of MSEFA, performance tests are conducted on the CEC 2013 and CEC 2019 test suites, after which MSEFA is used to solve four complex engineering optimization problems. Experimental results show that MSEFA has the best performance compared with other FA variants and other improved swarm intelligence algorithms. In addition, MSEFA also achieves the best results in dealing with four complex engineering optimization problems. • The proposed MSEFA algorithm can obtain the best results when dealing with optimization problems. • Three novel search strategies with different characteristics in this paper are presented. • A priority roulette selection method is designed to select strategies. [ABSTRACT FROM AUTHOR]

Energy conservation in buildings is of vital importance for solving the problem of global warming and energy scarcity in today's world. In this paper, we discuss the optimal control methods of the heating, ventilation, and air-conditioning (HVAC) system using swarm intelligence algorithms to reduce energy consumption of buildings. The performances of ten optimization algorithms are compared. The results on test functions manifest the artificial bee colony algorithm (ABC) exhibits the best performance among the focused optimization algorithms. Taking a real HVAC system as an example, the holistic energy models of equipment in the HVAC system are established and validated. Then, the focused algorithms are used for the optimal control of a real HVAC system aiming at energy conservation. The simulated results based on 24-h real operation data show that the energy-saving ratio through by the ABC can reach 24.07% on average. The work in this study will contribute to the building energy conservation and carbon neutral development in the future. • The performances of ten swarm intelligence optimization algorithms on the test function are compared. • A holistic model of a real heating ventilation and air conditioning (HVAC) system is developed. • The performances of ten optimization algorithms in the holistic energy consumption model are discussed. • The artificial bee colony (ABC) algorithm is applied to a real air-conditioning system for typical daily analysis. [ABSTRACT FROM AUTHOR]

• This paper used ANN hybridized with swarm intelligence algorithms to predict the carbonation resistance of RAC. • A comparison between the performance of proposed prediction models was performed. • The variable importance analysis and parametric analysis of input variables were studied. This paper investigates the prediction of carbonation depth for recycled aggregate concrete (RAC) with machine learning models. Nine parameters including RAC intrinsic properties and environmental conditions were considered as input variables. A dataset comprising 593 test data was used to train, validate, and test machine learning models. Results show that the Random forest model shows superior performance than the Gaussian progress regression model and standalone artificial neural network (ANN) model. All ANN models hybridized with swarm intelligence algorithms outperform the standalone ANN model, especially for the ANN model hybridized with the whale optimization algorithm. All machine learning models show higher accuracy than the existing code models and statistical models. The variable importance analysis shows that the carbonation resistance for RAC was determined by both internal and external factors. Based on the parametric analysis, the robustness of the proposed machine learning models was further confirmed. [ABSTRACT FROM AUTHOR]

• This paper proposes a way to optimize MQHOA to further improve its performance. • The use of information in heuristic algorithms is discussed in this paper. It can be used to improve the performance of other heuristic optimization algorithms. • The proposed algorithm was evaluated on a large number of test functions. The multiscale quantum harmonic oscillator algorithm (MQHOA) is a competitive heuristic optimization algorithm that has been successfully implemented in many applications. This paper proposes a novel way to optimize MQHOA to further improve its performance. The idea is to use the historical information in the evolutionary iterative process of the algorithm to derive a direction in the quantum harmonic oscillator (QHO) process and as a multi scale in the M process, then take the guidance information as the parameter to generate a new solution. The combination of these processes, i.e., MQHOA combined with the guidance information, is called GI-MQHOA. The experimental results show that the guidance information is of great significance for the exploration and exploitation of MQHOA. The proposed algorithm was evaluated on the CEC2014 test suite, and shown to be comparable to other state-of-the-art swarm intelligence and heuristic algorithms. The principle of using guiding information is simple and effective and can be easily transplanted to other heuristic algorithms. [ABSTRACT FROM AUTHOR]

In the present paper, we demonstrate the possibilities of designing quantum computing circuits using a specific swarm intelligence algorithm — iSOMA in the form of three experiments. All simulations are based on a simple sample of a quantum computing circuit from the Qiskit environment, which was used as a comparison circuit with the results of the three experiments already mentioned. In the first experiment, we try to find an arbitrary functional solution using iSOMA with minimal constraints on this circuit's design. It can be said that in this experiment, iSOMA showed the highest degree of "creativity". In the second experiment, we focused on whether iSOMA can be used to find a circuit identical to the one designed by a human or equivalent with the positions of the measurement gates fixed. In the last experiment, we highlight iSOMA's ability to avoid unnecessary qubit usage by adding redundant qubits to a possible circuit and fixing the measurement gates to the last two qubits in the scheme. In all three experiments, we see that iSOMA can find efficient functional and often astonishing solutions — the proposed method applied to a classical circuit founded a new one preserving required properties while saving one ancilla (redundant, useless, non-used) 1 1 https://en.wikipedia.org/wiki/Ancilla%5fbit. qubit. All computations are implemented in the IBM Qiskit 2 2 https://qiskit.org/learn/. environment. Although these are relatively simple experiments, the results show that evolutionary algorithms can successfully design more complex quantum circuits. [Display omitted] • Quantum circuit (QC) design by swarm intelligence. • A practical showcase in Python and IBM Qiskit environment. • 3 experiments demonstrating evolutionary algorithms performance in QC design. • Design of unusual QC topology saving one ancilla qubit. [ABSTRACT FROM AUTHOR]

A medical cyber-physical system (MCPS) integrates medical sensor devices with cyber (information) components, which creates a sensitive approach and provides security. The MCPS plays a vital role in hospitals by detecting attacks and protecting patients' medical information. Many research projects have been carried out to detect attacks on the generation of medical information in MCPS. The issues with existing algorithms are their inefficiency and time-consuming maximization of error rates. To overcome the challenges, this paper proposes detecting the attack using the fuzzy C-Means algorithm with artificial bee colony optimization (FCM-ABC). The novelty of the work is flexibility in deciding whether data points belong to actual users or attackers using the fuzzy c means degree [0,1] measuring technique of cluster formation. ABC is used in self-organizing the clusters with ABC collective intelligence. It monitors health information using sensor networks. The MCPS model interconnects the sensor devices remotely and collects the information. SVM has an accuracy rate of 76.32%, FCM has an accuracy rate of 81.34%, LSTM has an accuracy rate of 86.22%, and our proposed work, FCM-ABC, has an accuracy rate of 93.34%. [ABSTRACT FROM AUTHOR]

• Constructing a novel combined cubic generalized Ball (CCG-Ball, for short) surfaces. • Establishing the shape optimization model of CCG-Ball surfaces. • Solving the established model based on an enhanced jellyfish search (EJS) algorithm. • Comparing EJS algorithm with state-of-the-art algorithms for shape optimization. • EJS algorithm is competitive and superior to comparative algorithms. The construction and shape optimization of complex shape-adjustable surfaces is a crucial and intractable technique in Computer Aided geometric Design (CAGD), which has a wide range of application value in many product designs and manufacturing fields involving complex surfaces modeling. In this paper, a novel combined cubic generalized Ball (CCG-Ball, for short) surfaces is constructed and the shape optimization of CCG-Ball surfaces is studied by an enhanced jellyfish search (JS) algorithm. First and foremost, we construct the CCG-Ball surfaces with multiple shape parameters based on a class of cubic generalized Ball basis functions, and then derive the conditions of G1 and G2 continuity for the surfaces. The shapes of CCG-Ball surfaces can be adjusted and optimized expediently by utilizing their shape parameters. Secondly, the shape optimization of CCG-Ball surfaces is mathematically an optimization problem that can be efficiently dealt with by swarm intelligence algorithm. In this regard, an enhanced JS termed EJS algorithm, combined with sine and cosine learning factors, local escape operator, opposition-based learning and quasi-opposition learning strategies, is introduced to improve the convergence speed and calculation accuracy of the JS algorithm. Finally, by minimizing the energy of CCG-Ball surfaces as the evaluation standard, the shape optimization models of the surfaces with G1 and G2 geometric continuity are established, respectively. The EJS algorithm is utilized to solve the established models, and the CCG-Ball surfaces with minimum energy are obtained. The example results illustrate the ability of EJS algorithm in effectively solving the shape optimization problems of complex CCG-Ball surfaces. [ABSTRACT FROM AUTHOR]

Driver pathways have been acknowledged to play critical roles in the initiation and progression of cancers►hence it is essential for precision medicine related studies to develop accurate and efficient methods to identify them. Although previous approaches have shown promising results by integrating multi-omics data, their preset artificial parameters may decrease the convenience to use and limit the application scalability. In this paper, a novel integration approach is proposed to incorporate four omics data, i.e., construct a weighted non-binary mutation matrix without presetting artificial parameters. A parameter-free identification model is put forward based on it. It takes advantage of the association between genes in the PPI network as well as balances the contribution of coverage and mutual exclusivity with the harmonic mean. Furthermore, a cooperative co-evolution algorithm is proposed for solving this model. In the algorithm, a particle swarm optimization algorithm suitable for solving combinatorial problems is presented. Three cooperative operators are devised to construct the cooperation among the populations and the swarm to increase the population diversity. Both real biological datasets and simulated ones were exerted to perform experimental comparisons among the proposed method and six other state-of-the-art ones. The gene sets identified by the presented method generally contain more genes involved in known signaling pathways than those obtained by the other methods. Simultaneously, both high accuracy and high efficiency of the proposed method were verified in experiments, making it practical in realistic applications and an effective supplementary tool to identify driver pathways. • A non-binary mutation matrix is constructed without artificial adjustable parameters. • The model incorporates gene association and balances coverage and mutual exclusivity. • A PSO algorithm suitable for solving combinatorial problems is presented. • A co-operative co-evolution algorithm is put forward for solving the presented model. • The performance is compared among the proposed method and seven state of the art ones. [ABSTRACT FROM AUTHOR]

Target searching with autonomous robots require an efficient target search method that considers their constraints and environmental characteristics. Particle swarm optimization (PSO) is a fantastic population-based optimization algorithm. It is often used in swarm robotics cooperative search jobs because of its inspiration resources and velocity updating function. Given the global optimization features of PSO, it is simple to converge on a particular location in a search environment and miss out on opportunities to learn more. This paper proposes an adaptive exploration robotic PSO (AERPSO) to solve multi-target search problems. The proposed method enhances the chances of exploring unexplored regions and helps with obstacle avoidance using evolutionary speed and aggregation degree. The adaptive inertia weight helps in enhanced exploration. The simulation results compiled from various simulation experiments show that AERPSO performs way ahead of the existing state-of-the-art techniques for target searching. The proposed algorithm improves the search time by approximately 40% and the detection rate by 25% in comparison with existing approaches. It balances exploration and exploitation to become an excellent approach for multi-target searching. • To propose an adaptive exploration robotic PSO to search multiple targets. • The method avoids local minima and keeps the diversity among the population. • It enhances the exploration by adapting the inertia weights. • The proposed algorithm deals with overshooting problem of PSO. [ABSTRACT FROM AUTHOR]

• GBO-kNN is a proposed framework based on a wrapper approach for features selection. • The aim of the research is to reach maximum classification accuracy. • The performance of GBO-kNN is evaluated against real benchmark datasets. • The GBO-kNN performance is compared against seven recent metaheuristic algorithms. • Results showed high effectiveness on one dataset and moderate on another. Virtual Screening (VS) in drug discovery campaigns deals with enormously large databases containing massive redundant and/or irrelevant features. Thus, the pre-selection process of virtual screening requires developing a fast and precise classification methodology for filtering such a huge database. This paper proposed a framework depending on a wrapper selection approach for features selection. It consists of 1) an optimizer: Gradient-Based Optimizer (GBO), that hybridized with 2) the classifier: k-nearest neighbor (k-NN). The performance of the introduced framework, GBO-kNN, is estimated using real-world benchmark datasets; the QSAR Biodegradation which is composed of 41 features, and the Monoamine Oxidase (MAO) that consisted of 1665 features. the GBO-kNN framework is compared against seven recent swarm intelligence algorithms: Hybrid Harris Hawks Optimization Algorithm (HHO), Grey Wolf Optimization Algorithm (GWO), Butterfly Optimization Algorithm (BOA), Dragonfly Algorithm (DA), Moth-Flame Optimization Algorithm (MFO), Sine Cosine Algorithm (SCA), and Salp Swarm Algorithm (SSA) for reaching maximum classification accuracy. The experimental results exhibited that the proposed process, in comparison with other algorithms, achieved high effective accuracy of 98.8% over the high dimensional dataset (MAO) and a moderate effective performance over the low dimensional dataset (QSAR Biodegradation). The proposed framework source code is available at https://codeocean.com/capsule/9906421/tree/v1. [ABSTRACT FROM AUTHOR]

Swarm robotics is a growing field of swarm intelligence inspired by natural swarms for providing a solution for the collaboration among swarm agents to achieve a common goal. One of the challenges in designing a swarm robotic system with desired collective behaviour is to understand the effect of individual behaviour on group performance. This paper considers a system composed of autonomous swarm agents, each one executing its algorithm. We present a generic fault-tolerant pattern formation algorithm, in which different agents from different positions gather and then form a desired geometric pattern specifically a uniform circle and maintain it in the presence of faulty agents. We present an analysis based on simulations and experimental results of the proposed algorithm on swarm forming the pattern, in which failures are deliberately induced to check their fault tolerance capability and pattern formation capability. Our findings show that the formed geometric pattern can be restored and maintained depending on the different parameters in the presence of faulty agents. • We introduce a Fault-tolerance algorithm for pattern formation of swarm agents. • Elaborated the Uniform Circle formation and then fault-tolerance in uniform circle formation. • Used Sensor-based analysis as mathematical model. • Fault-tolerance algorithm is simulated and validated. [ABSTRACT FROM AUTHOR]

• A hybridization between Moth-Flame Optimization and Marine Predators Algorithms (MOMPA) is proposed • The proposed MOMPA is employed for solving the robot path planning problem with obstacle avoidance constraints. • After the validation experiment, MOMPA is applied for a case study on New Galala city in Egypt. New cities exploit the smartness of the IoT-based architecture to run their vital and organizational processes. The smart response of pandemic emergency response services needs optimizing methodologies of caring and limit infection without direct connection with patients. In this paper, a hybrid Computational Intelligence (CI) algorithm called Moth-Flame Optimization and Marine Predators Algorithms (MOMPA) is proposed for planning the COVID-19 pandemic medical robot's path without collisions. MOMPA is validated on several benchmarks and compared with many CI algorithms. The results of the Friedman Ranked Mean test indicate the proposed algorithm can find the shortest collision-free path in almost all test cases. In addition, the proposed algorithm reaches an almost %100 success ratio for solving all test cases without constraint violation of the regarded problem. After the validation experiment, the proposed algorithm is applied to smart medical emergency handling in Egypt's New Galala mountainous city. Both experimental and statistical results ensure the prosperity of the proposed algorithm. Also, it ensures that MOMPA can efficiently find the shortest path to the emergency location without any collisions. [ABSTRACT FROM AUTHOR]

This paper tackles the short-term hydro-power unit commitment problem in a multi-reservoir system — a cascade-based operation scenario. For this, we propose a new mathematical modeling in which the goal is to maximize the total energy production of the hydro-power plant in a sub-daily operation, and, simultaneously, to maximize the total water content (volume) of reservoirs. For solving the problem, we discuss the Multi-objective Evolutionary Swarm Hybridization (MESH) algorithm, a recently proposed multi-objective swarm intelligence-based optimization method which has obtained very competitive results when compared to existing evolutionary algorithms in specific applications. The MESH approach has been applied to find the optimal water discharge and the power produced at the maximum reservoir volume for all possible combinations of turbines in a hydro-power plant. The performance of MESH has been compared with that of well-known evolutionary approaches such as NSGA-II, NSGA-III, SPEA2, and MOEA/D in a realistic problem considering data from a hydro-power energy system with two cascaded hydro-power plants in Brazil. Results indicate that MESH showed a superior performance than alternative multi-objective approaches in terms of efficiency and accuracy, providing a profit of $412,500 per month in a projection analysis carried out. • An efficient Multi-objective Evolutionary Swarm Hybrid algorithm is proposed. • Development of a nonlinear model to operational control of Hydro-power plants. • Hydro-power plant data regression obtains the maximum efficiency of the power units. • Efficiency energy goals achieved an increasing the profit in the energy production. [ABSTRACT FROM AUTHOR]

For classification tasks in machine learning, this paper proposes a brand-new wrapper feature selection algorithm prototype named recursive elimination–election (REE), which is conceived in a simple but exquisite structure inspired by the recursion technique in computer science. Prevalent metaheuristic methods such as differential evolution (DE), particle swarm optimization (PSO), etc., from evolutionary computation (EC) and swarm intelligence (SI) communities have recently been widely applied to feature selection research, but suffer from severe drawbacks including but not limited to low-efficient binary representation transformation, poor population diversity, excessive control parameter adjustments and sophisticated mechanisms. Instead, REE is organically constructed with an ordinary subset representation of feature indexes, simple operators, getting rid of extra control parameters. Specifically, REE is assembled of two basic recursive sub-algorithms, i.e., recursive random bisection elimination (RRBE) and recursive greedy binary election (RGBE), which somewhat embody the idea of "divide-and-conquer". By inspecting smaller and potential feature subsets in recursive ways, better subsets are returned automatically. A comprehensive experimental study was conducted on 14 UCI and ASU benchmark datasets with feature sizes ranging from dozens to thousands by using REE together with 6 state-of-the-art metaheuristic algorithms for comparison. The results show that the proposed REE has competitive search ability for feature selection problems, and it is especially prominent in handling high-dimensional datasets. Therefore, REE is promising to become a wrapper feature selection search paradigm with low solution cost and high efficiency. • A brand-new wrapper feature selection algorithm based on recursion technique. • Only using set operations makes the algorithm easier than metaheuristic ones. • The algorithm does not get stuck into local optima, is able to explore more space. • More effective and scalable to handle high-dimensional datasets. [ABSTRACT FROM AUTHOR]

The new form of artificial intelligence training, i.e. federated learning, is becoming more popular in the last few years. It is an optimization problem that includes additional mechanisms such as aggregation and data transmission. In this paper, we propose a hybridization of this type of training with a meta-heuristic. The meta-heuristic algorithm is adapted to manage the entire process as well as to analyze the best models to minimize attacks on this type of collaboration. The proposed solution is based on minimizing the general model error, with additional control mechanisms for incoming models, or adapting the aggregation method depending on the quality of the model. The innovative solution has been analyzed in terms of its application to the problem of image classification using classical and convolutional neural networks, and the most popular meta-heuristic algorithms. The proposal was analyzed in terms of the accuracy of the general model as well as for security against poisoning attacks. We reached 91% of accuracy using the proposed method with the Red Fox Optimization Algorithm and 95% in terms of detection of poisoned samples in the database. • A hybridization of meta-heuristic methods with federated learning. • A meta-heuristic for managing training process of neural networks. • A new technique for analyzing models and choosing the best ones for aggregation. • Additional verification of models to reduce the likelihood of an attack by poisoning. • A mechanism for cleaning the data set in case of its poisoning. [ABSTRACT FROM AUTHOR]

• Proposed a hybrid algorithm as S-ROA for solving TSP robustly and effectively. • Integration of ROA and SHO accomplished to resolve TSP optimally. • Explorative and exploitative ability of S-ROA found to be remarkably superior. • Performance of S-ROA is verified over benchmark TSP instances. • Competence of S-ROA was proven over contemporary metaheuristic algorithms. Traveling Salesman Problem (TSP) is the combinatorial optimization problem, where a salesman starting from a home city travels all the other cities and returns to the home city in the shortest possible path. TSP is a popular problem because the instances of TSP can be applied to solve real-world problems, the implication of which turns TSP into a typical test bench for performance evaluation of novel algorithms. In the current years, different optimization algorithms inspired by biological groups have become very familiar. A combined intelligence of diverse social insects like bees, ants, birds, termites, fish, etc. has been analyzed to introduce multiple meta-heuristic algorithms in the field of swarm intelligence. The main intent of this paper is to develop a hybrid algorithm for solving the TSP robustly and effectively. In order to attain this challenging point, the objective model considered in this research work is the minimization of the distance of the salesman traveling through entire cities. Here, the optimal solution pertains to solve the TSP is to minimize the distance travelled by the salesman, which is determined based on the new hybrid optimization algorithm. This proposal plans to integrate the two well-performing optimization algorithms like Rider Optimization Algorithm (ROA) and Spotted Hyena Optimizer algorithm (SHO) to frame the new algorithm, Spotted Hyena-based Rider Optimization (S-ROA). Finally, the experimental results obtained by the hybrid algorithm to solve these TSP cases are benchmarked against the results obtained by using state-of-the-art algorithms and prove the competitive performance of the proposed model. [ABSTRACT FROM AUTHOR]

This paper proposes a new naturally inspired swarm intelligence algorithm called the Aptenodytes Forsteri Optimization Algorithm (AFO). The main inspiration is the emperor penguin's warm-hugging behaviour. When looking for a suitable location, emperor penguins need to sense the change of temperature, consider the location of other penguins, move closer to the centre of the penguin population, minimize their energy loss, and refer to their memory. These five strategies are evolved into five update modes of variables. According to the characteristics of these update modes in the exploration and exploitation stage, adaptive adjustment methods are designed to mix the five ways. The effectiveness of the proposed AFO is checked, through a comparison with other nature-inspired algorithms, on shifted classical benchmark problems and CEC2017 benchmark problems. Moreover, four engineering problems are utilized to estimate the effectiveness of AFO in optimizing constrained problems. The experimental results show that AFO has the best performance in most problems, which can be considered an excellent and competitive algorithm. In addition, as for the problem of partial algorithm update bias towards the origin, particular experiments and metrics are designed to examine the impact of such update methods on the generality of the algorithm. Source codes of AFO are publicly available at https://github.com/TwilightArchonYz/A-new-Nature-inspired-optimization-algorithm-AFO. [ABSTRACT FROM AUTHOR]

As the curse of dimensionality, band selection is utilized to choose part of feature information from hyperspectral image (HSI) and achieve the highest classification accuracy as possible. It is considered as a non-polynomial hard problem and difficult to be fully solved within the effective period, especially for traditional swarm optimization. Gray wolf optimizer (GWO) is a newly proposed swarm intelligence algorithm based on heuristic search, and chaotic operation helps the algorithm jump out of the local optima. In the paper, a novel band selection approach based on a modified GWO (MGWO) is presented to reduce the data dimension of HSIs, and chaotic operation is utilized to set the index of gray wolves. In addition, the weight of bands is updated by the best and worst fitness values as the iteration of MGWO, and evaluates the quality of each band. Experimental results illustrate that it is better than other state-of-the-art band selection approaches, and satisfied band subsets with the combination from 10% to 25% total number of bands are obtained with higher classification accuracy. • A MGWO is proposed to set the index of gray wolves and the parameter of transform function. • The weight of each band is updated by the best and worst fitness values as iteration. • A fixed number of bands is selected by the rank of weight to build band subset for HSI dataset. • A band selection approach based on MGWO is proposed, and OA keeps in satisfactory with fewer bands. [ABSTRACT FROM AUTHOR]

The Vehicle Routing Problem (VRP) is a classical NP-hard combinatorial optimization problem. In recent years, a lot of heuristic algorithms have been proposed for optimizing the problem, and many simulation and practical experiments are performed to evaluate and verify the effectiveness of different heuristic algorithms. In this paper, we focus on the Vehicle Routing Problem with stochastic demands (VRPSD) in which the customer demands follow known probability distributions. A hybrid algorithm called DSMO-GA which combines discrete spider monkey algorithm (SMO) with genetic algorithm (GA) is proposed for solving the VRPSD problem. In the proposed DSMO-GA for VRPSD, the individuals are coded as sets and sequences, and a conflict elimination method is designed to cancel the infeasible codes. Accordingly, a non adjacent 2-OPT is designed to enhance population diversity. The proposed DSMO-GA effectively integrates the advantages of DSMO and GA to further balance the global exploration and local exploitation capacity. Five groups of different experiments are conducted and the results show that DSMO-GA is valid for the VRPSD. Furthermore, the impact of various parameters on the performance of DSMO-GA are also investigated. [ABSTRACT FROM AUTHOR]

• Biologically inspired heuristics for target search lack modularity and modifiability. • The proposed hyper-heuristic is based on modular and adaptive swarm behavior. • Combining stigmergy, flocking and evolution outperforms bio-inspired heuristics. Target search aims to discover elements of various complexity in a physical environment, by minimizing the overall discovery time. Different swarm intelligence algorithms have been proposed in the literature, inspired by biological species. Despite the success of bio-inspired techniques (bio-heuristics), there are relevant algorithm selection and parameterization costs associated with every new type of mission and with new instances of known missions. In this paper, evolutionary optimization is proposed for achieving significant improvements of the mission performance. Although adaptive, the logic of bio-heuristics is nevertheless constrained by models of biological species. To generate more adaptable logics, a novel design approach based on hyper-heuristics is proposed, in which the differential evolution optimizes the aggregation and tuning of modular heuristics for a given application domain. A modeling and optimization testbed has been developed and publicly released. Experimental results on real-world scenarios show that the hyper-heuristics based on stigmergy and flocking significantly outperform the adaptive bio-heuristics. [Display omitted] [ABSTRACT FROM AUTHOR]

As an efficient and simple optimization algorithm, particle swarm optimization (PSO) has been widely applied to solve various real optimization problems. However, avoiding premature convergence and balancing the global exploration and local exploitation capabilities of the PSO remains two crucial problems. To overcome these drawbacks of PSO, a hybrid particle swarm optimization with crisscross learning strategy (PSO-CL) algorithm is proposed in this paper. In PSO-CL, in order to well balance the global exploration and local exploitation capabilities of PSO, a search direction adjustment mechanism based on subpopulation division operation is proposed. Meantime, to avoid the premature convergence and enhance the global search ability, a crossover-based comprehensive learning strategy (CCL) is adopted. Additionally, a stochastic example learning strategy (SEL) is introduced, which can assist collective information to be spread among separate sub-swarms, improve the local exploitation ability of the algorithm. 15 classic benchmark functions, CEC2017 test suite and two real-world optimization problems are utilized to verify the promising performance of PSO-CL, experimental results and statistical analysis indicate that PSO-CL has competitive performance compared with state-of-the-art PSO variants. [Display omitted] • A search direction adjustment mechanism based on subpopulation division operation. • A crossover-based comprehensive learning strategy is presented, which can enhance the diversity of swarm. • A stochastic example learning strategy is introduced to further enhance the convergence precise. [ABSTRACT FROM AUTHOR]

This paper presents an all-inclusive review of the optimization methods and their applications in fog computing. We put forward a taxonomy of the optimization techniques and then discuss their applications to solving various optimization problems in this field. Moreover, we develop a glossary of different optimization metrics in the literature and classify various evaluation environments used to test the solutions of different algorithms. The distribution of the relevant publications and the threats to the validity of the study are also discussed. Finally, we present the challenges in the existing literature and the future trends for research in fog computing optimization. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI) technologies are widely used to study the computational drug repositioning to find potential new uses for marketed drugs, which have a huge impact on drug development. However, due to the sparsity of datasets and the linear limitations of traditional AI or machine learning models lead to the inability of most methods to effectively obtain hidden feature of drugs and diseases To end this, Predictive Intelligence (PI) has been proposed in recent years. Hence, in this work, we propose a PI powered Attentional Stacking Matrix Factorization (PI-ASMF). Firstly, in order to extract the effective latent factor of the drugs or diseases the ASMF model superimposes the drug–disease similarity information and the drug–disease association information into the PI model, which ensures that it can learn the effective latent factor and alleviates the cold start problem to a certain extent. Subsequently, the latent factors of drugs and diseases are element-wise multiplied with their respective attentional vectors to learn the adaptive weights of each feature. Finally, the latent factors of drugs and diseases are fed into the prediction module to generate predictive values of potential drug–disease associations. The AUC and AUPR values of PI-ASMF model on two real datasets are 0.892 and 0.168, 0.913 and 0.25, respectively, which verified its superiority and validity. • The novel PI-ASMF model is proposed in this paper. • The PI-ASMF model can learn the effective drug or disease latent factor. • The PI-ASMF model can capture more complex drug–disease associations. • The results prove the effectiveness and superiority of the PI-ASMF model. [ABSTRACT FROM AUTHOR]

This paper proposes a modified marine predators algorithm based on quantum theory to handle the multilevel image segmentation problem. The main aims of using quantum theory is to enhance the ability of marine predators algorithm to find the optimal threshold levels to enhance the segmentation process. The proposed quantum marine predators algorithm gets the idea of finding a particle in the space based on a possible function borrowed from the Schrodinger wave function that determines the position of each particle at any time. This rectification in the search mechanism of the marine predators algorithm contributes to strengthening of exploration and exploitation of the algorithm. To analyze the performance of the proposed algorithm, we conduct a set of experiments. In the first experiment, the results of the developed quantum marine predators algorithm are compared with eight well-known meta-heuristics based on benchmark test functions. The second experiment demonstrates the applicability of the algorithm, in addressing multilevel threshold image segmentation. A set of ten gray-scale images assess the quality of the quantum marine predators algorithm and its performance is compared with other meta-heuristic algorithms. The experimental results show that the proposed algorithm performs well compared with other algorithms in terms of convergence and the quality of segmentation. • Improving Marine Predators Algorithm using the Quantum theory. • Using the proposed method as global optimization method. • Compare the performance of proposed with other well-known Metaheuristic methods. • Assess the quality of proposed approach using twenty-three problems and ten Benchmark images. [ABSTRACT FROM AUTHOR]

Intelligent optimization algorithms based on swarm principles have been widely researched in recent times. The Firefly Algorithm (FA) is an intelligent swarm algorithm for global optimization problems. In literature, FA has been seen as an efficient and robust optimization algorithm. FA is an algorithm that has obtained good to best results in complex problems. Therefore, there are many instances of modification, and hybridization of FA with other optimizing algorithms, but further improvements are still possible. This research first proposes a new modification of FA by introducing a novel and unique stepping ahead parameter. The concept is based on being proactive rather than reactive, which is the normal behavior of a standard FA. The notion behind stepping ahead is to send a firefly ahead then the best known position to look for even better solution. Second, a new design of a hybrid of the newly modified FA with Covariance Matrix Adaptation Evolution Strategy (CMAES) to improve the exploitation further while maintaining good exploration in the fireflies is presented. The main use of CMAES in this hybrid algorithm is to provide diversity to fireflies, as a result it improves exploitation. Traditionally, hybridization has combined two or more algorithms in terms of structure only, and consideration was not given to the increase in time complexity or diversity. In this paper, the two algorithms are not run in separate cycles rather CMAES is placed inside the FA. Through this novelty, CMAES is initiated inside FA loop and an extra loop is avoided and at the same time CMAES diversifies FA solutions. The structure of algorithm together with the strength of individual solution are used. The newly established modified FA and hybrid are used to solve selected sixty five global optimization benchmark problems together with eight real-world problems from CEC 2011. The proposed algorithms have outperformed FA algorithm in both benchmark and real-world problems. The optimal solutions found by FA in benchmark problems was 69.2% while FA-Step algorithm achieved 73.9% and FA-CMAES algorithm obtained 92.3%. In real-world problem, FA obtained 37.5%, FA-Step was 50% while FA-CMAES was 75%. The results invariably show that the proposed algorithms perform significantly better than the standalone methods as well as the algorithms from the literature. • This research introduces a new and novel stepping ahead Firefly Algorithm (FA-Step). • Another proposed method is hybrid of Stepping Ahead FA with CMAES (FA-CMAES). • Proposed methods solve 65 global optimization problems and 8 real-world problems. • FA-Step and FA-CMAES achieve 73.9% and 92.3% best results in benchmark problems. • In real-world problems, FA-CMAES is able to find 7 out of 8 optimal results. [ABSTRACT FROM AUTHOR]

Differential evolution is an effective and practical approach that is widely applied for solving global optimization problems. Nevertheless, its effectiveness and scalability are decreased when the problems' dimension is increased. Hence, this paper is devoted to proposing a novel DE algorithm named quantum-based avian navigation optimizer algorithm (QANA) inspired by the extraordinary precision navigation of migratory birds during long-distance aerial paths. In the QANA, the population is distributed by partitioning into multi flocks to explore the search space effectively using proposed self-adaptive quantum orientation and quantum-based navigation consisted of two mutation strategies, DE/quantum/I and DE/quantum/II. Except for the first iteration, each flock is assigned using an introduced success-based population distribution (SPD) policy to one of the quantum mutation strategies. Meanwhile, the information flow is shared through the population using a new communication topology named V-echelon. Furthermore, we introduce two long-term and short-term memories to provide meaningful knowledge for partial landscape analysis and a qubit-crossover operator to generate the next search agents. The effectiveness and scalability of the proposed QANA were extensively evaluated using benchmark functions CEC 2018 and CEC 2013 as LSGO problems. The results were statistically analyzed by the Wilcoxon signed-rank sum test, ANOVA, and mean absolute error tests. Finally, the applicability of the QANA to solve real-world problems was evaluated by four engineering problems. The experimental results and statistical analysis prove that the QANA is superior to the competitor DE and swarm intelligence algorithms in test functions CEC 2018 and CEC 2013, with overall effectiveness of 80.46% and 73.33%, respectively. • Proposing an optimizer algorithm (QANA) inspired by migratory birds' navigation behaviors. • Introducing LTM and STM memories to share information and partial landscape analysis. • Introducing a V-echelon topology to spread information flow throughout search agents. • Introducing a quantum mutation strategy equipped by two meaningful search strategies. • The proposed QANA optimizer is very competitive and superior to compared algorithms. [ABSTRACT FROM AUTHOR]