With the rapid development of sixth-generation (6G) wireless communication technologies, conventional wireless communication networks are gradually reaching maturity, and there is an urgent need to explore next-generation communication technologies to meet the demands of the big-data information era. Intelligent Reflecting Surface (IRS) technology has emerged as one of the key enabling technologies for advancing $\mathbf{6 G}$ communications. Motivated by this, we investigate the achievable rate optimization problem in an IRS-assisted multiple-input multiple-output (MIMO) communication system. In this paper, an improved Frilled Lizard Optimization (FLO)-based algorithm is proposed. By incorporating spatial pyramid mapping-based population initialization, guided local search, a crowding strategy,, the original FLO is significantly enhanced. Simulation results demonstrate that the improved frilled lizard optimization algorithm achieves substantial performance gains in terms of achievable rate maximization.

In RIS-assisted wireless communication systems, accurate angle information is essential for beamforming and channel estimation. However, in dynamic channel environments, variations in the Angle of Departure (AoD) can degrade the performance of conventional estimation methods. To address this issue, this paper proposes a RIS phase optimization method based on an Improved Artificial Rabbits Optimization (iARO) algorithm to enhance the angle tracking accuracy of the Extended Kalman Filter (EKF). First, a RIS-assisted dynamic angle tracking model is established, where the real and imaginary parts of the complex channel gain together with the AoD are considered as state variables, and EKF is applied for recursive estimation. Then, taking the mean square error (MSE) of the EKF angle estimation as the objective function, the iARO algorithm is employed to globally optimize the phase configuration of RIS elements, thereby improving the sensitivity of the received signal to angle variations. The proposed algorithm incorporates opposition-based learning initialization, global-best neighborhood search, gravitational guidance, and an iterative restart strategy, which enhance global search capability and convergence stability.

With the rapid development of 5G wireless communication technology and the continuous evolution of the digital society, traditional wireless communication networks are gradually approaching maturity, with their system capacity based on Shannon’s theorem nearing theoretical limits. Simultaneously, the burgeoning growth of artificial intelligence and the demand for intelligent communication in 6G networks have driven the resurgence of semantic communication technologies, making them one of the forefront research topics in contemporary communication technologies. This paper proposes a resource allocation strategy based on an improved Frilled Lizard Optimization Algorithm (FLO). By integrating immune network-based population initialization, memory mechanisms, reverse elite strategies, and fitness difference adjustment strategies, the FLO is significantly improved. The proposed strategy aims to maximize semantic spectrum efficiency in a text transmission semantic communication system. By jointly optimizing channel allocation and the number of transmitted semantic symbols, this strategy demonstrates superior performance in resource allocation, providing theoretical and practical support for the further development of semantic communication systems.

The Lion Swarm Optimization (LSO) algorithm tends to become trapped in local optima due to its reliance on inter-group collaboration during population iteration updates, particularly between lionesses and cubs in their position update strategy. Moreover, the king primarily performs limited exploration in its vicinity, thereby inevitably compromising the search for the global optimum. Its randomness and locality also produce numerous ineffective solutions. To address this issue, we refine the king's behavior by incorporating strategies from the Whale Optimization Algorithm (WOA) and introduce the rogue lion to confront the king during iterations, facilitating escape from local optima. Additionally, we employ dynamic learning strategies to enhance the position update functions of lionesses and cubs, diminishing their excessive interdependence and preventing entrapment in local optima. Furthermore, comparative evaluations on unimodal and multimodal test functions demonstrate that the improved algorithm converges rapidly. Finally, due to the limited application scenarios of the original LSO algorithm, we effectively applied the Lion Swarm-Whale Hybrid Optimization (LSWO) algorithm to optimize airport ground handling (AGH) operations. In comparison to the original algorithm, the improved version better utilizes resources and enhances operational efficiency.

With the continuous advancement of wireless communication technologies and the development and maturity of 5 G systems, research into next-generation (6 G) communication technologies has gradually intensified. Among the primary research directions in 6G communication, Reconfigurable Intelligence Surface (RIS) have gained widespread attention. RIS is a key technology that actively controls the wireless signal propagation environment through electromagnetic modulation techniques. By incorporating programmable reflective components into the communication environment, RIS enhances signal quality and coverage, thus improving the efficiency and performance of communication systems. Energy efficiency is a crucial metric for RIS-assisted communication systems. It aims to maximize energy efficiency (EE) by controlling the switching states of each RIS unit and optimizing power consumption between the base station and RIS. In this paper, we enhance the Frilled Lizard Optimization Algorithm by integrating strategies such as Latin Hypercube Sampling. These improvements are designed to enhance the algorithm's performance. Simulation results indicate that the improved Frilled lizard Optimization Algorithm significantly improves energy efficiency maximization in a Multiple-Input, Single-Output (MISO) communication system assisted by RIS-controlled unit states.

The Flexible job shop scheduling problem (FJSP) is a well-known challenge in operations research, involving the allocation of jobs to machines while minimizing makespan. Traditional priority-based scheduling rules (PDRs) often suffer from limitations like deadlock, especially in dynamic environments. Deep reinforcement learning (DRL) has shown promise in overcoming some of these challenges, but it requires complex large-scale graph structures that lead to slow convergence and limited exploration of the solution space. To address these issues, this paper proposes a novel approach that integrates multi-graph learning with iterative optimization, called the Feature-Guided intelligent optimizer (FGIO) for FJSP. In our approach, we propose a Multi-Graph feature aggregation-oriented (MG-FAO) structure that reduces node density and enhances the sensitivity of the disjunctive graph to critical paths, efficiently capturing both explicit constraints and implicit topological relationships. To accelerate the optimization rate, we integrate the node information extracted from the MG-FAO structure into the intelligent optimization strategy of the Panoptic migrating birds optimization (PMBO) algorithm through an encoding-decoding operation, ensuring dynamic adaptation to evolving scheduling constraints and providing intelligent guidance for decision-making within the PMBO algorithm. This unprecedented bidirectional cybernetic coupling mechanism allows for better exploration and exploitation of the solution space. Extensive experiments demonstrate that the FGIO significantly outperforms existing heuristic, meta-heuristic, and DRL-based methods, even when applied to new, unseen datasets. Moreover, we introduce the concept of the ideal makespan as a theoretical upper bound, providing a more rigorous evaluation benchmark for FJSP instances. Our results highlight the power of integrating multi-graph learning with swarm intelligence, offering a promising solution to one of the most challenging optimization problems in modern manufacturing and operations management.

In wireless sensor networks (WSNs), optimal node deployment and energy-efficient routing are critical to prolonging network lifetime and improving data transmission reliability. This paper proposes a novel collaborative optimization framework that integrates Lion Swarm Optimization (LSO) and Reinforcement Learning (RL) to jointly solve the node deployment and routing problems in WSNs. LSO is utilized to determine the initial optimal positions of sensor nodes, ensuring full coverage and connectivity while minimizing deployment cost. Subsequently, RL dynamically optimizes routing paths based on real-time energy consumption and network state feedback. The integration of LSO and RL enables adaptive and intelligent decision-making in both spatial and temporal domains. Simulation results demonstrate that the proposed method significantly outperforms conventional techniques in terms of network lifetime, energy balance, and data delivery ratio. This approach provides a promising solution for intelligent and sustainable WSN management.

In the information age, many practical problems involve dynamic objective functions that change over time or due to other factors. Optimizing in such dynamic environments is crucial both theoretically and practically, as it addresses prominent challenges in optimization today. For instance, dynamic vehicle path planning requires adapting to changing vehicle and road conditions to determine the optimal route. In image processing, dynamic multi-objective segmentation can enhance recognition accuracy by addressing changing image data. In engineering, designing dynamic welded beams involves adjusting for changes in material properties over time. Traditional lion swarm optimization (LSO) algorithms often struggle with dynamic environments, tending to optimize based on outdated conditions and missing global optimal solutions. This paper improves LSO by incorporating dynamic particle swarm optimization mechanisms and the black-winged kite algorithm's hunting and elimination strategies to better track global optima in changing environments. The performance of the improved dynamic lion swarm algorithm is evaluated using the four-peak DF1 dynamic environment model, enhanced by the bimodal DFI model.

This study introduces a framework for clustering competition coevolution optimization algorithm based on the parallel Lion Swarm Optimization Algorithm (LSO). This framework combines clustering and competitive coevolution concepts under existing parallel computing paradigms. Initially, clustering categorizes particles of the total population, followed by parallel computing principles where particles within each classified subpopulation undergo local optimization using distinct optimization mechanisms. After a certain number of iterations, these subpopulations coevolve through an island-based topology. Experimental results demonstrate significant advantages of the proposed algorithm over traditional methods in both CEC2013 benchmark functions and feature selection problems, affirming its potential and effectiveness in practical applications. This framework introduces a novel approach and method for addressing complex problems, offering broad prospects for application.

Integrated sensing and communication (ISAC) technology as a research focus in 6G communications commonly works in high frequency band, which may suffer severe fading caused by obstacle. Reconfigurable intelligent surface (RIS) can overcome the above issue and improve the performance of ISAC system through phase adjustment. In this paper, dual-RIS assisted 3D positioning and beamforming design in ISAC system are studied. Firstly, the localization in the ISAC system is transformed into a compressed sensing (CS) problem, and a stepwise matching pursuit (SMP) algorithm is proposed for better positioning ability and lower complexity, compared with the typical matching pursuit (MP) algorithm. Then, the positioning information is utilized for the beamforming design of the RISs to maximize the system achievable rate through the alternating optimization algorithm based on the triangle inequality (TI-AO). Simulation results show that the system achievable rate of the optimization design is close to the optimal one and verifies the effectiveness of the proposed framework.

Addressing the limitations of the Lion Swarm Optimization (LSO) algorithm, such as its tendency to converge to local optima and its slow convergence rate, we propose an improved LSO algorithm that integrates Gaussian mapping and a somersault foraging strategy. Firstly, we advocate replacing the randomly generated initial population of the original algorithm with chaotic sequences generated via Gaussian mapping, thereby augmenting the diversity within the population. Secondly, the incorporation of the somersault foraging strategy is aimed at enhancing the diversity of optimization positions, thus bolstering the algorithm's resilience against local optima. Simulation experiments conducted on CEC2019 benchmark functions showcase notable enhancements in both convergence speed and solution accuracy with our proposed algorithm. Finally, the application of the improved LSO algorithm to multi-focus image fusion tasks reveals its superior performance in quantitative and visual assessments when compared against conventional techniques and genetic algorithm.

After the random deployment of wireless sensor nodes, issues such as overlapping coverage and detection blind spots inevitably arise. Employing intelligent optimization algorithms to optimize node deployment and expand network coverage is a common approach. However, the high-dimensional optimization problem of sensor node deployment and the complexity of coverage areas make it difficult for conventional intelligent optimization algorithms to achieve satisfactory solutions. To address these challenges, this paper proposes a wireless sensor network coverage optimization scheme based on a Lion Swarm Optimization (LSO) algorithm with a crisscross strategy. By introducing both horizontal and vertical crisscrosses of individuals during the population evolution process, the diversity of the population is increased, enhancing the algorithm's global search capability and ability to escape local optima. Results demonstrate that the improved LSO algorithm exhibits enhanced solution accuracy and convergence compared to the standard LSO algorithm. In the context of wireless sensor network coverage optimization, this algorithm provides a better design solution.

With the maturation and widespread adoption of 5G communication network technology, research into next-generation (6G) communication technology has intensified to meet the demands of data transmission in the big data era. Reflective Intelligent Surface (RIS) technology has emerged as a promising approach for advancing 6G communication capabilities. The objective of employing intelligent reflective surfaces in wireless network transmission is to optimize beamforming at the base station transmitter and the reflection coefficient of the RIS. This optimization aims to maximize the weighted sum-rate (WSR) at the user side, while adhering to constraints related to base station transmitter power and RIS unit modes. In this paper, we propose enhancements to the basic lion swarm optimization algorithm by incorporating elements such as a good point set, chaotic search, and a mining mechanism inspired by the honey badger algorithm. These modifications are intended to improve the algorithm's performance in optimizing the aforementioned parameters. Simulation results demonstrate that our enhanced lion swarm optimization algorithm achieves significant improvements in weighting sum rate maximization, thereby enhancing the efficiency of reflective intelligent surface-assisted wireless networks.

With the rapid development of UAV edge computing, the randomness of task generation and the unpredictability of UAV mobility have made related problems increasingly complex. This not only poses a highly intricate integer optimization problem but also requires swift and effective decision-making based on real-time monitoring. Traditional offline algorithms face numerous challenges in addressing such issues and often struggle to meet the demands of dynamic environments. In response, this paper proposes an innovative heuristic algorithm-the V nderwater Lobster Optimization Algorithm-combined with reinforcement learning techniques to dynamically learn the optimal data transmission path. Through this approach, we can flexibly adjust the optimization algorithm's update strategy, effectively achieving dynamic management goals and improving the system's overall performance and response speed. The proposed algorithm successfully addresses the task offloading problem in UAV edge computing. Experimental results show that the algorithm significantly reduces system response time and improves task completion rates, fully demonstrating its potential and advantages in the field of AV edge computing.

During the iterative process, the probability of selection is directly linked to the fitness magnitude, as each iteration of swarm intelligence optimization algorithms progressively converges towards an optimal solution. Building upon this premise, we introduce the Lion Swarm Optimization Algorithm based on the Proportional Strategy (PLSO), designed to enhance convergence speed and achieve superior optima. Inspired by the roulette strategy, our approach integrates the concept into the Lion Swarm Optimization (LSO) algorithm. In essence, it mimics the behavior of lion cubs, who learn hunting by following lionesses; however, their choice of lioness to follow is governed by rationality, favoring those with superior hunting skills. This fosters knowledge transfer among lionesses, strengthening the link between global and local optima, thereby enhancing local search capabilities and significantly accelerating convergence. In this paper, we evaluate the performance of the PLSO algorithm against six single-peak test functions, four multi-peak test functions, and four additional functions, chosen randomly. Comparative analyses are conducted with classical optimization algorithms, and the PLSO algorithm is applied to address the image segmentation problem. Our findings demonstrate the superior efficacy and robustness of the PLSO algorithm.

With the development of information science, a large amount of data has poured into people's lives. As one of the effective means of extracting massive data, feature selection has been concerned by a large number of scholars. Feature selection is an NP-hard problem, and one of the traditional methods is to use optimization algorithms to search. However, this traditional method faces serious challenges with the increase of feature- dimensional data. This is mainly because as the feature dimension becomes larger, the search space of the optimization algorithm will increase exponentially, which will seriously degrade the search performance. Previous studies have shown that evolutionary algorithms assisted by surrogate models can solve high-dimensional feature selection well. Following this research line of thought, this paper proposes a surrogate-assisted cooperative lion swarm optimization algorithm for high- dimensional feature selection, which combines co-evolution and surrogate assistance with the lion swarm optimization algorithm to improve the accuracy of high-dimensional feature selection problems. Experiments show that the algorithm can show excellent performance in feature selection problems up to 6000 dimensions.

Abstract: When solving multimodal optimization problems, the lion swarm optimization (LSO) algorithm will face many problems, such as low individual diversity, slow search speed, premature convergence or even falling into local extremum. The traditional approach is to introduce chaotic search, gaussian mutation or other mutation strategies to enhance the local search ability of the LSO algorithm. These improvements have been verified and the performance of the algorithm can be improved to a certain extent. However, these improved strategies lack effective use of population information, which will still affect the performance of the algorithm in search speed and search accuracy. To solve this problem, on the basis of the above-mentioned improved algorithm, this paper introduces the distribution estimation algorithm and proposes a hybrid improved LSO algorithm. The hybrid improved algorithm analyzes and learns the structure of the problem by constructing a probability model for the dominant group, and guides the efficient optimization of individuals in the population according to this information. It is verified in five standard test functions and ten test functions of IEEE CEC 2021. Compared with the traditional improved LSO algorithm, the results show that the hybrid improved LSO algorithm is superior.