Generalized opposition-based artificial bee colony algorithm

Abstract

The Artificial Bee Colony (ABC) algorithm is a relatively new algorithm for function optimization. The algorithm is inspired by the foraging behavior of honey bees. In this work, the performance of ABC is enhanced by introducing the concept of generalized opposition-based learning. This concept is introduced through the initialization step and through generation jumping. The performance of the proposed generalized opposition-based ABC (GOABC) is compared to the performance of ABC and opposition-based ABC (OABC) using the CEC05 benchmarks library.

Artificial bee colony–based spectrum handoff algorithm in wireless cognitive radio networks

“…10 After each possible source location is generated and assessed by the honey bee, the performance of that source is compared with the previous one. 20 If it is not possible to improve a food source for a scheduled number of cycles that is known as limit, that food source is abandoned by the employed bee. 8 If not, the previous source is kept in the memory.…”

“…14,15 Artificial bee colony algorithm replicates the behaviors of the real honey bees for searching food sources and sharing the knowledge of food sources among honey bees. 20,21 In our work, a new ABC-based spectrum handoff algorithm for wireless cognitive radio systems is proposed. 18,19 Besides, ABC is better than other population-based algorithms with the advantage of using fewer control parameters.…”

“…18,19 Besides, ABC is better than other population-based algorithms with the advantage of using fewer control parameters. 20,21 In our work, a new ABC-based spectrum handoff algorithm for wireless cognitive radio systems is proposed. In our work, some secondary users must sense the spectrum so that the interrupted secondary user may perform spectrum handoff process.…”

Work-In-Progress: Adaptive Population Artificial Bee Colony for Numerical Optimization

“…In 2012, EI-Abd proposed an interesting ABC variant named GOABC [7]. They introduced an opposition-based learning and involved generalized concept into ABC for enhancing optimizer's performance.…”

Probabilistic properties of fitness-based quasi-reflection in evolutionary algorithms

“…More recent opposition-based EAs include appending artificial bee colony algorithm [14] with opposition to form generalized oppositionbased ABC [15]; CODEQ, a parameter-free algorithm that combines chaotic search, opposition-based learning, differential evolution and quantum mechanics for optimizing constrained problems [16]; and opposition-based gravitational search algorithm [17].…”