Novel Bat Algorithm (NBA)

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Novel Bat Algorithm (NBA)

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Novel Bat Algorithm (NBA) represents an advanced evolution of the classic Bat Algorithm (BA) in the field of metaheuristic optimization. This innovative approach introduces two key improvements that significantly enhance its performance.

First, NBA employs a multi-swarm strategy where each individual bat follows distinct search patterns. Unlike traditional BA where all bats share identical behaviors, this diversification allows for more thorough exploration of the solution space. Some bats may prioritize global exploration while others focus on local refinement, creating a balanced search dynamic.

Second, the algorithm incorporates a self-adaptive local search mechanism. During optimization, bats autonomously adjust their search intensity based on real-time performance feedback. When nearing promising solutions, they automatically intensify local exploitation; conversely, they broaden their search scope in less optimal regions. This adaptability eliminates the need for manual parameter tuning common in other metaheuristics.

Comparative studies demonstrate NBA's superior convergence speed and solution accuracy against conventional BA and other nature-inspired algorithms. Its hybrid approach combining population diversity with adaptive precision makes it particularly effective for complex, high-dimensional optimization problems in engineering design, machine learning hyperparameter tuning, and operational research scenarios.