Ordered Fuzzy Numbers Applied in Bee Swarm Optimization Systems

Abstract

The paper presents an innovative OFNBee optimization method based on combining the swarm intelligence with the use of directed fuzzy numers OFN. In the introduction, the issues related to the subject of the study, including bee algorithms and OFN numbers, were reviewed. The innovative OFNBee algorithm was presented and verified against a set of known benchmarks functions such as Sphere, Rastrigin, Griewank, Rosenbrock, Schwefel and Ackley. These functions have been applied due to their reliability in the literature. In the further part of the study, the configuration of the algorithm parameters is carried out, including the launch of each mathematical function several dozen times for different data, such as different population sizes. The key part of the research and analysis was to compare OFNBee with six standard ABC, MBO, IMBO, TLBO, HBMO, BBMO bee algorithms. The article ends with a summary and an indication of the possible future works.

A New Artificial Duroc Pigs Optimization Method Used for the Optimization of Functions

“…At the beginning, pigs are "sent" to NP = 20 random places. This number of locations (20) resulted in the best overall performance in both the experiments reported in what follows and in other completed ones. However, it is important to note that NP should be seen as one of the hyperparameters of the proposed approach.…”

“…• modification of the Artificial Bee Colony (ABC) algorithm by application of Ordered Fuzzy Numbers (OFN) -OFNBee [19,20], or • Hybrid Fuzzy-Ant Colony optimization [21].…”

“…Since the results of combining OFNs with ABC optimization were quite promising [19,20], further exploration of this research direction (exploring hybrid optimization approaches) ensued. In this contribution, the behavior of a pig herd was selected as the nature-originating phenomenon.…”

Leveraging Swarm Intelligence for Optimal Thermal Camera and Sensor Placement in Industrial Environments

“…An alternative approach is based on numerous meta-heuristic methods, such as genetic algorithms, evolution strategies, swarm optimization, and simulated annealing. The investigators leading this study possess extensive expertise in artificial intelligence algorithms, particularly emphasizing swarm intelligence and nature-inspired techniques, optimization methodologies, control systems, and fuzzy numbers [24][25][26][27][28][29][30][31][32][33][34][35]. This paper delves into the exploration of applying the bat algorithm to identify the optimal camera and motion sensor placement within a complex industrial environment.…”