Bi-objective jellyfish algorithm for team formation problem.

Area of Science:

• Computer Science
• Optimization Algorithms
• Swarm Intelligence

Background:

• Team Formation (TF) problems are complex, requiring optimal expert selection for cost-efficiency and task completion.
• TF problems often involve multiple, distinct objectives that need simultaneous optimization.
• Existing methods struggle with the multi-objective nature and complexity of TF problems.

Purpose of the Study:

• To address the multi-objective nature of Team Formation problems.
• To propose a novel algorithm, Chaotic Jellyfish Search with Enhanced Swap Operator (CJSESOS), for bi-objective optimization.
• To enhance exploration and escape local optima in swarm intelligence algorithms.

Main Methods:

• Formulated the Team Formation problem as a bi-objective optimization problem.
• Developed the Chaotic Jellyfish Search with Enhanced Swap Operator (CJSESOS) algorithm, enhancing the Jellyfish Search Optimizer (JSO).
• Incorporated a chaotic sequence (logistic map) for solution diversity and an enhanced swap operator to escape local optima.

Main Results:

• The proposed Bi-CJSESOS algorithm demonstrated superior performance in finding optimal teams.
• Bi-CJSESOS effectively satisfied dual objectives (e.g., cost and fault discovery rate) compared to other algorithms.
• Evaluated effectiveness using benchmark functions and a dataset with varying skill requirements.

Conclusions:

• The Bi-CJSESOS algorithm is effective for solving bi-objective Team Formation problems.
• The enhancements significantly improve exploration and the ability to find Pareto-optimal solutions.
• This novel approach offers a more efficient method for constructing optimal expert teams.