Bio-inspired optimisation algorithms have recently attracted much attention in the control community. Most of these algorithms mimic particular behaviours of some animal species in such a way that allows solving optimisation problems. The present paper aims at applying three metaheuristic methods for optimising fuzzy logic controllers used for quadrotor attitude stabilisation. The investigated methods are particle swarm optimisation (PSO), BAT algorithm and cuckoo search (CS). These methods are applied to find the best output distribution of singleton membership functions of the fuzzy controllers. The quadrotor control requires measured responses, therefore, three objective functions are considered: integral squared error, integral time-weighted absolute error and integral time-squared error. These metrics allow performance comparison of the controllers in terms of tracking errors and speed of convergence. The simulation results indicate that BAT algorithm demonstrated higher performance than both PSO and CS. Furthermore, BAT algorithm is capable of offering 50% less computation time than CS and 10% less time than PSO. In terms of fitness, BAT algorithm achieved an average of 5% better fitness than PSO and 15% better than CS. According to these results, the BAT-based fuzzy controller exhibits superior performance compared with other algorithms to stabilise the quadrotor.

This paper presents a new hybrid self-adaptive harmony search combined with a stochastic local search algorithm (SAHS-SLS) to solve the 0-1 multidimensional knapsack problem (MKP). The proposed SAHS-SLS uses SAHS to create harmonies that will be improved with SLS. We propose a dynamic adjustment of the walk probability (wp) in SLS and a technique to compute the bandwidth (bw) and the pitch adjusting rate (PAR) in SAHS. The overall method SAHS-SLS is implemented and evaluated on benchmarks in order to measure its performance in solving the MKP. It is compared to other approaches to show its effectiveness. The numerical results are encouraging and demonstrate the benefit of the proposed approach.

This paper presents an improved version of Genetic Algorithm (GA) to solve the 0-1 Multidimensional Knapsack Problem (MKP01), which is a well-known NP-hard combinatorial optimisation problem. In combinatorial optimisation problems, the best solutions have usually a common partial structure. For MKP01, this structure contains the items with a high values and low weights. The proposed algorithm called Genetic Algorithm Guided by Pretreatment information (GAGP) calculates these items and uses this information to guide the search process. Therefore, GAGP is divided into two steps, in the first, a greedy algorithm based on the efficiency of each item determines the subset of items that are likely to appear in the best solutions. In the second, this knowledge is utilised to guide the GA process. Strategies to generate the initial population and calculate the fitness function of the GA are proposed based on the pretreatment information. Also, an operator to update the efficiency of each item is suggested. The pretreatment information has been investigated using the CPLEX deterministic optimiser. In addition, GAGP has been examined on the most used MKP01 data-sets, and compared to several other approaches. The obtained results showed that the pretreatment succeeded to extract the most part of the important information. It has been shown, that GAGP is a simple but very competitive solution.