  Bat Algorithm (BA) is a nature-inspired metaheuristic search algorithm
designed to efficiently explore complex problem spaces and find near-optimal
solutions. The algorithm is inspired by the echolocation behavior of bats,
which acts as a signal system to estimate the distance and hunt prey. Although
the BA has proven effective for various optimization problems, it exhibits
limited exploration ability and susceptibility to local optima. The algorithm
updates velocities and positions based on the current global best solution,
causing all agents to converge towards a specific location, potentially leading
to local optima issues in optimization problems. On this premise, this paper
proposes the Modified Bat Algorithm (MBA) as an enhancement to address the
local optima limitation observed in the original BA. MBA incorporates the
frequency and velocity of the current best solution, enhancing convergence
speed to the optimal solution and preventing local optima entrapment. While the
original BA faces diversity issues, both the original BA and MBA are
introduced. To assess MBAs performance, three sets of test functions (classical
benchmark functions, CEC2005, and CEC2019) are employed, with results compared
to those of the original BA, Particle Swarm Optimization (PSO), Genetic
Algorithm (GA), and Dragonfly Algorithm (DA). The outcomes demonstrate the MBAs
significant superiority over other algorithms. Additionally, MBA successfully
addresses a real-world assignment problem (call center problem), traditionally
solved using linear programming methods, with satisfactory results.
