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Team Formation (TF) problems represent one of the most significant areas in computer science and optimization. The challenge lies in forming the best team of experts capable of completing a specific task at the lowest cost, which is a highly complex problem. Furthermore, the TF problem involves multiple attributes, each of which can be treated as a distinct objective that needs to be optimized. The problem solution varies according to the specified objectives. In this study, the TF problem is formulated as a bi-objective optimization problem, and a novel algorithm, Chaotic Jellyfish Search with Enhanced Swap Operator (CJSESOS), is proposed. This method is based on the Jellyfish Search Optimizer (JSO), a recent swarm intelligence algorithm known for its superior performance in various optimization tasks. CJSESOS introduces two major enhancements: (1) a chaotic sequence generated via a logistic map to improve solution diversity and exploration (CJSO), and (2) an enhanced swap sequence operator that increases the algorithm's ability to escape local optima. The CJSESOS algorithm was adapted to enhance its exploration capabilities during the search for Pareto-optimal solutions. The effectiveness of the proposed bi-objective Chaotic Jellyfish search optimizer (Bi-CJSESOS) was evaluated using a different dataset with different skill numbers, in addition to twelve benchmark functions. Experimental results showed that, for the same fault discovery rate, the Bi-CJSESOS can find an optimal team and satisfy the two objectives more than the other comparative algorithms.
Salam et al. (Fri,) studied this question.