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The development of combination of proportional-integral-derivative and proportional- derivative (PID-PD) controller for overhead crane is presented. Due to the pendulum-like settings, the swinging of load has caused many difficulties while operating the overhead crane. Swinging of the load causes unnecessary tension to the cable and structure of the overhead crane, which will compromise the safety of operator and other workers. Overhead cranes should have the ability to move the load to desired point as fast as possible while minimizing the load swing and maintaining the accuracy. Proportional-integral-derivative (PID) controller is used for overhead crane positioning and proportional-derivative (PD) controller for load oscillation. New time-domain performance criterion function is used in particle swarm optimization (PSO) algorithm for the tuning of the PID-PD controller rather than the general performance criteria using error of the system. This performance criterion function monitors the performance in terms of rise time, overshoot, settling time and steady state error of the overhead crane system. The performance of the optimised PID-PD controller is verified with simulation in MATLAB. The PSO optimized PID-PD controllers with new performance criterion are shown effective in improving the step response of the overhead crane position as well as controlled the load oscillation.

Bats sonar algorithm (BSA) as a swarm intelligence approach utilises the concept of echolocation of bats to find prey. However, the algorithm is unable to achieve good precision and fast convergence rate to the optimum solution. With this in mind, an adaptive bats sonar algorithm is introduced with new paradigms of real bats echolocation behaviour. The performance of the algorithm is validated through rigorous tests with several single objective optimisation benchmark test functions. The obtained results show that the proposed scheme outperforms the BSA in terms of accuracy and convergence speed and can be efficiently employed to solve engineering problems.

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A modified adaptive bats sonar algorithm (MABSA) is presented that utilises the concept of echolocation of a colony of bats to find prey. The proposed algorithm is applied to solve the constrained optimisation problems coupled with penalty function method as constraint handling technique. The performance of the algorithm is verified through rigorous tests with four constrained optimisation benchmark test functions. The acquired results show that the proposed algorithm performs better to find optimum solution in terms of accuracy and convergence speed. The statistical results of MABSA to solve all the test functions also has been compared with the results from several existing algorithms taken from literature on similar test functions. The comparative study has shown that MABSA outperforms other establish algorithms, and thus, it can be an efficient alternative method in the solving constrained optimisation problems.

An integrated algorithm for solving multi-objective optimisation problems using a dual- level searching approach is presented. The proposed algorithm named as dual-particle swarm optimisation-modified adaptive bats sonar algorithm (D-PSO-MABSA) where the concept of echolocation of a colony of bats to find prey in the modified adaptive bats sonar algorithm is combined with the established particle swarm optimisation algorithm. The proposed algorithm combines the advantages of both particle swarm optimisation and modified adaptive bats sonar algorithm approach to handling the complexity of multi-objective optimisation problems. These include swarm flight attitude and swarm searching strategy. The performance of the algorithm is verified through several multi- objective optimisation benchmark test functions. The acquired results show that the proposed algorithm performs well to produce a reliable Pareto front. The proposed algorithm can thus be an effective method for solving multi-objective optimisation problems.