Search

In this paper, a new population initialization method for metaheuristic algorithms is proposed. In our approach, the initial set of candidate solutions is obtained through the sampling of the objective function with the use of the Metropolis–Hastings technique. Under this method, the set of initial solutions adopts a value close to the prominent values of the objective function to be optimized. Different from most of the initialization methods that consider only spatial distribution, in our algorithm, the initial points represent promising regions of the search space, which deserve to be exploited to identify the global optimum. These characteristics allow the proposed approach obtains a faster convergence and improves the quality of the produced solutions. With the objective to demonstrate the capacities of our initialization method, it has been embedded in the classical Differential Evolution algorithm. To evaluate its performance, the complete system has been tested in a set of representative benchmark functions extracted from several datasets. Experimental results show that the proposed technique presents a better convergence speed and an increment in the quality of the solutions than other similar approaches. [ABSTRACT FROM AUTHOR]

This paper presents a powerful swarm intelligence metaheuristic optimization algorithm called Dynamic Cat Swarm Optimization. The formulation is through modifying the existing Cat Swarm Optimization Algorithm. The original Cat Swarm Optimization suffers from the shortcoming of "premature convergence," which is the possibility of entrapment in local optima which usually happens due to the off balance between exploration and exploitation phases. Therefore, the proposed algorithm suggests a new method to provide a proper balance between these phases by modifying the selection scheme and the seeking mode of the algorithm. To evaluate the performance of the proposed algorithm, 23 classical test functions, 10 modern test functions (CEC 2019) and a real-world scenario are used. In addition, the dimension-wise diversity metric is used to measure the percentage of the exploration and exploitation phases. The optimization results show the effectiveness of the proposed algorithm, which ranks first compared to several well-known algorithms available in the literature. Furthermore, statistical methods and graphs are also used to further confirm the outperformance of the algorithm. Finally, the conclusion and future directions to further improve the algorithm are discussed. [ABSTRACT FROM AUTHOR]

Feature transformation methods can be used effectively to improve the performance of the machine learning techniques, such as classification and clustering, via enhancing the discrimination among the samples. Additionally, binary representation of features may be necessary in several circumstances. For example, distributed systems may have limited bandwidth, storage, and energy that necessitate rough quantization of the measurements. This paper proposes a random projection-based feature transformation method that maps the data points from the original space into a new binary space. In the proposed transformation method, the random projection process is formulated as an optimization problem. A modified chaotic version of the sine–cosine algorithm is used to find the optimal random projector. The performance of the proposed transformation method is validated using 15 standard UCI benchmark datasets against the linear discernment analysis. Moreover, it is compared to other metaheuristic-based feature reduction methods including the gray wolf optimizer, the genetic algorithm, and the antlion optimizer. The obtained results assure the superiority of the proposed transformation method in terms of classification accuracy and storage requirements. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid bio-inspired metaheuristic optimization approach namely emperor penguin and salp swarm algorithm (ESA) is proposed. This algorithm imitates the huddling and swarm behaviors of emperor penguin optimizer and salp swarm algorithm, respectively. The efficiency of the proposed ESA is evaluated using scalability analysis, convergence analysis, sensitivity analysis, and ANOVA test analysis on 53 benchmark test functions including classical and IEEE CEC-2017. The effectiveness of ESA is compared with well-known metaheuristics in terms of the optimal solution. The proposed ESA is also applied on six constrained and one unconstrained engineering problems to evaluate its robustness. The results reveal that ESA offers optimal solutions as compared to the other competitor algorithms. [ABSTRACT FROM AUTHOR]

We address one of the external factors of personnel inventory behavior, deployments. The configuration of persistent unit deployments has the ability to affect everything from individual perceptions of service palatability to operational effectiveness. There is little evidence to suggest any analytical underpinnings to the U.S. Army deployment scheduling and unit assignment patterns. This paper shows that the deployment scheduling and unit assignment problem can be formulated as an interval graph such that modifications to traditional graph coloring algorithms provide an efficient mechanism for dealing with multiple objectives. [ABSTRACT FROM AUTHOR]

Swarm algorithms belong to the class of population metaheuristic optimization methods. Despite the use of various metaphors, most swarm algorithms have similar structures, where one can distinguish common components such as the decision population initialization, decision diversification, and decision intensification. Based on the concept of generality, an analysis of key approaches to, methods for, and ways of increasing the efficiency of swarm optimization algorithms was carried out. In the survey, swarm optimization algorithms are viewed as a set of operators without a detailed discussion of each algorithm. The main focus is on the analysis of the key components of the algorithms. The main idea behind efficiency improvement is to maintain a balance between diversification and intensification. In this context, we consider mechanisms for supporting population diversity, methods for tuning and adjusting the swarm algorithm parameters, and approaches to hybridization of algorithms. We also indicate several open problems related to the topic of the survey. [ABSTRACT FROM AUTHOR]