New Caledonian crow learning algorithm: A new metaheuristic algorithm for solving continuous optimization problems

A horizontal and vertical crossover cuckoo search: optimizing performance for the engineering problems

As science and technology advance, more engineering-type problems emerge. Technology development has likewise led to an increase in the complexity of optimization problems, and the need for new optimization techniques has increased. The swarm intelligence optimization algorithm is popular among researchers as a flexible, gradient-independent optimization method. The cuckoo search (CS) algorithm in the population intelligence algorithm has been widely used in various fields as a classical optimization algorithm. However, the current CS algorithm can no longer satisfy the performance requirements of the algorithm for current optimization problems. Therefore, in this paper, an improved CS algorithm based on a crossover optimizer (CC) and decentralized foraging (F) strategy is proposed to improve the search ability and the ability to jump out of the local optimum of the CS algorithm (CCFCS). Then, in order to verify the performance of the algorithm, this paper demonstrates the performance of CCFCS from six perspectives: core parameter setting, balance analysis of search and exploitation, the impact of introduced strategies, the impact of population dimension, and comparison with classical algorithms and similar improved algorithms. Finally, the optimization effect of CCFCS on real engineering problems is tested by five classic cases of engineering optimization. According to the experimental results, CCFCS has faster convergence and higher solution quality in the algorithm performance test and maintains the same excellent performance in engineering applications.

An Improved New Caledonian Crow Learning Algorithm for Global Function Optimization

The New Caledonian crow learning algorithm (NCCLA) is a novel metaheuristic algorithm inspired by the learning behavior of New Caledonian crows learning to make tools to obtain food. However, it suffers from the problems of easily falling into local optima and insufficient convergence accuracy and convergence precision. To further improve the convergence performance of NCCLA, an improved New Caledonian crow learning algorithm (INCCLA) is proposed in this paper. By determining the parent individuals based on the cosine similarity, the juveniles are guided to search toward different ranges to maintain the population diversity; a novel hybrid mechanism of complete and incomplete learning is proposed to balance the exploration and exploitation capabilities of the algorithm; the update strategy of juveniles and parent individuals is improved to enhance the convergence speed and precision of the algorithm. The test results of the CEC2013 and CEC2020 test suites show that, compared with the original NCCLA algorithm and four of the best metaheuristics to date, INCCLA has significant advantages in terms of convergence speed, convergence precision, and stability.

Wild Geese Migration Optimization Algorithm: A New Meta-Heuristic Algorithm for Solving Inverse Kinematics of Robot

This paper proposes a new meta-heuristic algorithm, named wild geese migration optimization (GMO) algorithm. It is inspired by the social behavior of wild geese swarming in nature. They maintain a special formation for long-distance migration in small groups for survival and reproduction. The mathematical model is established based on these social behaviors to solve optimization problems. Meanwhile, the performance of the GMO algorithm is tested on the stable benchmark function of CEC2017, and its potential for dealing with practical problems is studied in five engineering design problems and the inverse kinematics solution of robot. The test results show that the GMO algorithm has excellent computational performance compared to other algorithms. The practical application results show that the GMO algorithm has strong applicability, more accurate optimization results, and more competitiveness in challenging problems with unknown search space, compared with well-known algorithms in the literature. The proposal of GMO algorithm enriches the team of swarm intelligence optimization algorithms and also provides a new solution for solving engineering design problems and inverse kinematics of robots.

Optimized Feature Selection in Software Product Lines using Discrete Bat Algorithm

Software Product Lines (SPLs) are one of the ways to develop software products by increasing productivity and reducing cost and time in the software development process. In SPLs, each product has many features and it is necessary to consider the optimal and custom features of the products. In fact, selecting key features in SPLs is a challenging process. Feature selection in SPLs is an optimization problem and an NP-Hard problem. One way to select a feature is to use meta-heuristic algorithms modeled on nature, i.e., Bat Algorithm. By modeling bat behavior in prey hunting, a suitable meta-innovative algorithm is considered. This algorithm has important advantages that make it more accurate than conventional methods such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm. In this paper, to select software product features, idol binary algorithm and artificial neural network are used to identify important features of software products that reduce production costs and time. The experiments in MATLAB software and datasets related to software production lines show that the rate of reduction of target performance error or feature selection cost in software production lines in the proposed method has decreased by 64.17% with increasing population.