Artificial Searching Swarm Algorithm (ASSA) is a new optimization algorithm. ASSA simulates the soldiers to search an enemy’s important goal, and transforms the process of solving optimization problem into the proces...Artificial Searching Swarm Algorithm (ASSA) is a new optimization algorithm. ASSA simulates the soldiers to search an enemy’s important goal, and transforms the process of solving optimization problem into the process of searching optimal goal by searching swarm with set rules. This work selects complicated and highn dimension functions to deeply analyse the performance for unconstrained and constrained optimization problems and the results produced by ASSA, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Fish-Swarm Algorithm (AFSA) have been compared. The main factors which influence the performance of ASSA are also discussed. The results demonstrate the effectiveness of the proposed ASSA optimization algorithm.展开更多
The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical image...The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of medicalimage datasets for identifying the targeted region of interest.展开更多
In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, ...In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.展开更多
将人工鱼群算法应用于软硬件划分，从而提出一种软硬件划分方法．针对人工鱼群算法在应用于离散型问题时普遍存在的最优解出现概率低、收敛速度慢等问题，采用随机步长来改善鱼的游走行为，使用邻域搜索来获得邻域内的更优状态，并根据...将人工鱼群算法应用于软硬件划分，从而提出一种软硬件划分方法．针对人工鱼群算法在应用于离散型问题时普遍存在的最优解出现概率低、收敛速度慢等问题，采用随机步长来改善鱼的游走行为，使用邻域搜索来获得邻域内的更优状态，并根据无效迭代次数来提前终止迭代、提高算法效率．在对不同结点数的随机 DAG 图划分实验中，改进后算法的平均耗时约为原算法的6.5%～34.5%，而最优解出现概率则为原算法的5～7倍．因此，改进后算法在寻优能力和收敛速度上均优于原始算法，可更高效地完成软硬件划分任务．展开更多
针对人工鱼群算法(artificial fish swarm algorithm,AFSA)多峰寻优能力不足的问题,提出了一种免疫人工鱼群网络算法。应用改进的觅食行为,提升了算法的局部寻优能力;采用免疫网络调节机理,保持了人工鱼群多样性,不断探寻新的局部峰值;...针对人工鱼群算法(artificial fish swarm algorithm,AFSA)多峰寻优能力不足的问题,提出了一种免疫人工鱼群网络算法。应用改进的觅食行为,提升了算法的局部寻优能力;采用免疫网络调节机理,保持了人工鱼群多样性,不断探寻新的局部峰值;执行模式搜索法(pattern search method,PSM),完成精英人工鱼群的精细搜索。仿真实验结果表明,该算法具有较强全局优化能力和局部优化能力,且搜索到每个最优解都达到了理想值。展开更多
文摘Artificial Searching Swarm Algorithm (ASSA) is a new optimization algorithm. ASSA simulates the soldiers to search an enemy’s important goal, and transforms the process of solving optimization problem into the process of searching optimal goal by searching swarm with set rules. This work selects complicated and highn dimension functions to deeply analyse the performance for unconstrained and constrained optimization problems and the results produced by ASSA, Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Fish-Swarm Algorithm (AFSA) have been compared. The main factors which influence the performance of ASSA are also discussed. The results demonstrate the effectiveness of the proposed ASSA optimization algorithm.
基金the Researchers Supporting Project(RSP2023R395),King Saud University,Riyadh,Saudi Arabia.
文摘The distinction and precise identification of tumor nodules are crucial for timely lung cancer diagnosis andplanning intervention. This research work addresses the major issues pertaining to the field of medical imageprocessing while focusing on lung cancer Computed Tomography (CT) images. In this context, the paper proposesan improved lung cancer segmentation technique based on the strengths of nature-inspired approaches. Thebetter resolution of CT is exploited to distinguish healthy subjects from those who have lung cancer. In thisprocess, the visual challenges of the K-means are addressed with the integration of four nature-inspired swarmintelligent techniques. The techniques experimented in this paper are K-means with Artificial Bee Colony (ABC),K-means with Cuckoo Search Algorithm (CSA), K-means with Particle Swarm Optimization (PSO), and Kmeanswith Firefly Algorithm (FFA). The testing and evaluation are performed on Early Lung Cancer ActionProgram (ELCAP) database. The simulation analysis is performed using lung cancer images set against metrics:precision, sensitivity, specificity, f-measure, accuracy,Matthews Correlation Coefficient (MCC), Jaccard, and Dice.The detailed evaluation shows that the K-means with Cuckoo Search Algorithm (CSA) significantly improved thequality of lung cancer segmentation in comparison to the other optimization approaches utilized for lung cancerimages. The results exhibit that the proposed approach (K-means with CSA) achieves precision, sensitivity, and Fmeasureof 0.942, 0.964, and 0.953, respectively, and an average accuracy of 93%. The experimental results prove thatK-meanswithABC,K-meanswith PSO,K-meanswith FFA, andK-meanswithCSAhave achieved an improvementof 10.8%, 13.38%, 13.93%, and 15.7%, respectively, for accuracy measure in comparison to K-means segmentationfor lung cancer images. Further, it is highlighted that the proposed K-means with CSA have achieved a significantimprovement in accuracy, hence can be utilized by researchers for improved segmentation processes of medicalimage datasets for identifying the targeted region of interest.
基金supported by the National Natural Science Foundation of China(61472441)
文摘In this paper, a static weapon target assignment（WTA）problem is studied. As a critical problem in cooperative air combat,outcome of WTA directly influences the battle. Along with the cost of weapons rising rapidly, it is indispensable to design a target assignment model that can ensure minimizing targets survivability and weapons consumption simultaneously. Afterwards an algorithm named as improved artificial fish swarm algorithm-improved harmony search algorithm（IAFSA-IHS） is proposed to solve the problem. The effect of the proposed algorithm is demonstrated in numerical simulations, and results show that it performs positively in searching the optimal solution and solving the WTA problem.
文摘将人工鱼群算法应用于软硬件划分，从而提出一种软硬件划分方法．针对人工鱼群算法在应用于离散型问题时普遍存在的最优解出现概率低、收敛速度慢等问题，采用随机步长来改善鱼的游走行为，使用邻域搜索来获得邻域内的更优状态，并根据无效迭代次数来提前终止迭代、提高算法效率．在对不同结点数的随机 DAG 图划分实验中，改进后算法的平均耗时约为原算法的6.5%～34.5%，而最优解出现概率则为原算法的5～7倍．因此，改进后算法在寻优能力和收敛速度上均优于原始算法，可更高效地完成软硬件划分任务．
