This paper treats a tuning of PID controllers method using multiobjective ant colony optimization. The design objective was to apply the ant colony algorithm in the aim of tuning the optimum solution of the PID controllers (K p , K i , and K d ) by minimizing the multiobjective function. The potential of using multiobjective ant algorithms is to identify the Pareto optimal solution. The other methods are applied to make comparisons between a classic approach based on the "Ziegler-Nichols" method and a metaheuristic approach based on the genetic algorithms. Simulation results demonstrate that the new tuning method using multiobjective ant colony optimization has a better control system performance compared with the classic approach and the genetic algorithms.

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The problem of efficiently scheduling production jobs on several machines is an important consideration when attempting to make effective use of a multimachines system such as a flexible job shop scheduling production system (FJSP). In most of its practical formulations, the FJSP is known to be NP-hard [8][9], so exact solution methods are unfeasible for most problem instances and heuristic approaches must therefore be employed to find good solutions with reasonable search time. In this paper, two closely related approaches to the resolution of the flexible job shop scheduling production system are described. These approaches combine the Ant system optimisation meta-heuristic (AS) with local search methods, including tabu search. The efficiency of the developed method is compared with others.

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The use of wind energy for electric power generation provides a clean and renewable source. Therefore there is an increasing interest in developing and exploiting natural energy generation system. Switched reluctance generators (SRGs) have the potential to be a robust and highly efficient electrical conversion system for variable-speed wind applications. This study presents a new approach for optimising performance of a SRG intended for variable-speed direct drive wind turbine applications. DC bus voltage level and phase voltage switching angles have been identified as control variables affecting power generation. Owing to highly non-linear characteristics of SRG, iterative simulation of the generator model on the range of control variables can be used for finding output power profile. Since it is a multidimensional search space, the number of iterations is very big. Differential evolution (DE) strategy has been introduced to find optimal firing angles and DC bus voltage level under multiple operating conditions. Optimisation of the control variables is performed using a machine model based on the measured characteristics. Selected operating points are experimentally tested using a 4 kW 1500 rpm SRG prototype. DE algorithm is a viable alternative for generating optimal control in multidimensional optimisation of SRG wind energy generation.