Response surface methodology is a common tool in optimizing processes. It mainly concerns situations when there is only one response of interest. However, many designed experiments often involve simultaneous optimization of several quality characteristics. This is called a Multiresponse Surface optimization problem. A common approach in dealing with these problems is to apply desirability function approach combined with an optimization algorithm to determine the best settings of control variables. As the response surfaces are often nonlinear and complex a number of meta-heuristic search techniques have been widely for optimizing the objective function. Amongst these techniques genetic algorithm, simulated annealing, tabu search and hybridization of them have drawn a great deal of attention so far This study presents the use of harmony search algorithm for Multiresponse surface optimization. It is one of the recently developed meta-heuristic algorithms that has been successfully applied to several engineering problems. This music inspired heuristic is conceptualized from the musical process of searching for a perfect state of harmony. The performance of the algorithm is evaluated by an example from the literature. Results indicate the efficiency and outperformance of the method in comparison with some previously used methods.

This paper presents a social harmony search algorithm to solve optimization problems with continuous design variables. Although the harmony search (HS) algorithm (HSA) has proven its ability in finding near global regions within a reasonable time, it is rather inefficient in performing local search. The proposed method applies the harmony search optimizer for global optimization and normal distribution is employed to update the position of each design variable of a new harmony found by the first rule of the HS (memory consideration) in every stage to rapidly attain the feasible solution space. Normal distribution works as a global search in early iterations and as a local search in final iterations to improve HS in order to quickly converge and find better solutions. Various benchmark optimization problems are used to illustrate the effectiveness and robustness of the proposed method. Finally, the experimental results reveal the superiority of the proposed method in quick convergence and finding better solutions compared to the classic HS, its recently developed variants, and some other optimization algorithms.

This paper presents a strategy for using harmony search algorithm in facility layout optimization problems. In this paper an adapted harmony search algorithm is developed for solving facility layout optimization problems. This method finds an optimal facility arrangement in an existing layout. Two real-world case studies are employed to demonstrate the efficiency of this model. A comparison is also made to illustrate the efficiency of these strategies in facility layout optimization.

Many optimization techniques have been proposed since the inception of engineering optimization in 1960s. Traditional mathematical modeling-based approaches are incompetent to solve the engineering optimization problems, as these problems have complex system that involves large number of design variables as well as equality or inequality constraints. In order to overcome the various difficulties encountered in obtaining the solution of these problems, new techniques called metaheuristic algorithms are suggested. These techniques are numerical optimization algorithms that are based on a natural phenomenon. In this study, a state-of-art improved harmony search method with a new adaptive error strategy is proposed to handle the design constraints. Number of numerical examples is presented to demonstrate the efficiency of the proposed algorithm in solving engineering optimization problems.

The harmony search (HS) algorithm is a popular metaheuristic optimization method that reproduces the music improvisation process in searching for a perfect state of harmony. HS has a remarkable ability in detecting near global optima at low computational cost but may be ineffective in performing local search. This study presents the Multi- Adaptive harmony search (MAHS) algorithm for sizing optimization of skeletal structures with continuous or discrete design variables. The main difference between the proposed algorithm and classic HS is the way of choosing and adjusting the bandwidth distance (bw). Furthermore, MAHS dynamically updates the harmony Memory Consideration Rate (HMCR) and Pitch-Adjusting Rate (PAR) parameters during the search process. The robustness and performance of the MAHS algorithm are evaluated in comparison with literature, and in particular, with well-known HS variants such as Global-best harmony search (GHS), Self-Adaptive harmony search (SAHS), and Efficient harmony search (EHS). optimization results obtained by the MAHS algorithm confirm the validity of the proposed approach.

This study suggests a structure which is based on developing a mathematical model for simulation of complex relationships among water, soil, plant, and climate along with the optimization of irrigation allocation and crop pattern planning. SWAT model was employed to bear the task of simulation and HS algorithm took care of the optimization of the water usage and crop pattern. Also, two scenarios were aimed to investigate this approach. In the first scenario (base), the crop pattern was the variable of the model while in the second one it was predefined as the existing situation in the target plain. The objective function of the coupled model was to maximize the net profit subject to the total available land and water constraints. Results obtained by the developed model showed that the net profit gained in the plain has a potential of increase up to 213% and 23% in the first and second scenario, respectively. The water consumption decreased 28% and 48% respectively for the first and second scenarios comparing to the present situation of the plain. Accordingly, the performance of the HS-SWAT model indicated its capability for solving the optimum crop pattern and irrigation planning problems.