Task Scheduling With Hybrid Particle Swarm and Cuckoo Search
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Solution Overview
Problem
The traditional particle swarm optimization algorithm has insufficient local search capability in late iterations and is prone to getting stuck in local optimal solutions, limiting its global search ability.
Innovation Solution
Introduce a cuckoo search algorithm to enhance the particle swarm optimization algorithm by determining when it falls into a local optimal solution and using the cuckoo search algorithm to improve global search capability, employing a hybrid approach that iterates particles using both algorithms separately.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the particle swarm optimization algorithm is used for task scheduling, then the convergence speed is fast in early iteration, but the local search capability is insufficient in late iteration and it is easily caught in local optimal solutions
Solution Approach 1:
The patent combines particle swarm optimization algorithm and cuckoo search algorithm into a hybrid algorithm. The particle swarm optimization provides fast convergence in early iterations, while the cuckoo search algorithm is introduced in late iterations to enhance local search capability and escape local optimal solutions, thus resolving the contradiction between convergence speed and local search capability.
Solution Approach 2:
The patent implements periodic switching between different algorithms based on iteration stage. In early iterations, particle swarm optimization is used for fast convergence; in late iterations, cuckoo search algorithm is activated to improve local search. This periodic action allows the system to leverage the strengths of each algorithm at appropriate stages.
2Device complexity
If the particle swarm optimization algorithm is used for task scheduling, then the algorithm structure is simple, but the ability to jump out of local optimal solution is insufficient
Solution Approach 1:
The patent merges particle swarm optimization algorithm with cuckoo search algorithm to create a hybrid algorithm that maintains the simplicity of particle swarm optimization while adding the Lévy flight mechanism from cuckoo search. This combination enables the algorithm to jump out of local optimal solutions without significantly increasing structural complexity.
Solution Approach 2:
The cuckoo search algorithm acts as an intermediary mechanism that is introduced when the particle swarm optimization algorithm gets stuck in local optimal solutions. The Lévy flight mechanism serves as a mediator to guide particles out of local optima and explore new regions of the search space.
Data Source
AI summary
The present application discloses a task scheduling method based on an improved particle swarm optimization algorithm, which includes: obtaining task data to be scheduled, encoding particles according to the task data; iterating the particles by a particle swarm optimization algorithm; in response to that the particle swarm optimization algorithm does not fall into a local optimal solution, outputting a scheduling scheme; and in response to that the particle swarm optimization algorithm falls into the local optimal solution, outputting the scheduling scheme by fusing the particle swarm optimization algorithm with a cuckoo search algorithm. The present application introduces a cuckoo search algorithm when the particle swarm optimization algorithm falls into a local optimal solution, solving the dilemma of the particle swarm optimization algorithm falling into a local optimal solution, while improving the global search capability of the algorithm.