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This article presents a novel Comprehensive Learning Bat Algorithm (CLBAT) for the optimal coordinated design of Power System Stabilizers (PSSs) and Static Var Compensator (SVC) for damping electromechanical oscillations in multimachine power systems considering wide range of operating conditions. The CLBAT incorporates a new comprehensive learning strategy (CLS) to improve the micro-bats cooperation, location update is also improved to maintain the bats diversity and to prevent premature convergence through a novel adaptive search strategy based on the relative travelled distance. In addition, the proposed elitist learning strategy (ELS) speeds up the convergence during the optimization process and drives the global best solution (gbest) toward promising regions. The superiority of the CLBAT over recent algorithms is first demonstrated via several experiments and comparisons through benchmark functions. The developed algorithm ensures convergence speed, credibility, computational resources, and optimal tuning of PSSs and SVCs of multimachine systems under different operating conditions through eigenanalysis, nonlinear simulation, and performance indices.

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