A Novel Fuzzy Clustering Method Based on Chaos Small-World Algorithm for Image Edge Detection

Abstract-To solve the fuzzy edge detection problems in image processing, a novel fuzzy clustering method based on chaos smallworld algorithm (CSWFCM) is presented. The traditional fuzzy clustering method (FCM) is good at local searching capability, but it is sensitive to the initial value and easy to trap into local minimum value. The small-world algorithm (SWA), inspired by the mechanism of small-world phenomenon, is a novel global searching algorithm, which enables to enhance the diversity of the population and avoid trapping into local minimum value. However, the further capability of solving complicated problems is limited for its low efficiency of local short-range searching operator. In this paper, the chaos disturbance is utilized to improve the searching efficiency of SWA after local short-range search, and the chaos small-world algorithm (CSWA) is used to optimize the FCM in image edge detection. The simulation results show that the proposed algorithm can correctly detect the fuzzy and exiguous edges with higher convergence speed

A spring search algorithm applied to engineering optimization problems

At present, optimization algorithms are used extensively. One particular type of such algorithms includes random-based heuristic population optimization algorithms, which may be created by modeling scientific phenomena, like, for example, physical processes. The present article proposes a novel optimization algorithm based on Hooke’s law, called the spring search algorithm (SSA), which aims to solve single-objective constrained optimization problems. In the SSA, search agents are weights joined through springs, which, as Hooke’s law states, possess a force that corresponds to its length. The mathematics behind the algorithm are presented in the text. In order to test its functionality, it is executed on 38 established benchmark test functions and weighed against eight other optimization algorithms: a genetic algorithm (GA), a gravitational search algorithm (GSA), a grasshopper optimization algorithm (GOA), particle swarm optimization (PSO), teaching–learning-based optimization (TLBO), a grey wolf optimizer (GWO), a spotted hyena optimizer (SHO), as well as an emperor penguin optimizer (EPO). To test the SSA’s usability, it is employed on five engineering optimization problems. The SSA delivered better fitting results than the other algorithms in unimodal objective function, multimodal objective functions, CEC 2015, in addition to the optimization problems in engineering

The blue monkey: A new nature inspired metaheuristic optimization algorithm

This paper introduces a study to Blue Monkey (BM) algorithm, which is a new metaheuristic algorithm optimization based on the performance of blue monkey swarms in nature. The BM algorithm identifies how many males in one group. Normally, outside the season of the breeding, the groups of blue monkeys have only one adult male like other forest guenons. In addition to related patas monkeys (Erythrocebus patas). Forty-three of well-known test functions, which used in the area of optimization are used as benchmark to check BM algorithm, in addition, BM verified by a comparative performance check with Artificial-Bee-Colony (ABC), Gravitational Search Algorithm (GSA), Biogeography-Based Optimizer (BBO), and Particle Swarm Optimization (PSO). The obtained results demonstrated that BM algorithm is competitive compared with the selected metaheuristic algorithms; also, BM is able to converge towards the global optimal through optimization problems. Further, this algorithm is very efficient in field of dissolving real problems with restrictions and unidentified search space. It should be mentioned that the BM algorithm has some variables and it can obtain better results. in many test functions comparing with other algorithms