In this study, to enhance the optimization process, especially in the structural engineering field two well-known algorithms are merged together in order to achieve an improved hybrid algorithm. These two algorithms are Teaching-Learning Based Optimization (TLBO) and Harmony Search (HS) which have been used by most researchers in varied fields of science. The hybridized algorithm is called A Di...

In this paper, a new method is proposed for solving the data clustering problem using Cat Swarm Optimization (CSO) algorithm based on chaotic behavior. The problem of data clustering is an important section in the field of the data mining, which has always been noted by researchers and experts in data mining for its numerous applications in solving real-world problems. The CSO algorithm is one ...

Most optimization problems in real life applications are often highly nonlinear. Local optimization algorithms do not give the desired performance. So, only global optimization algorithms should be used to obtain optimal solutions. This paper introduces a new nature-inspired metaheuristic optimization algorithm, called Hoopoe Heuristic (HH). In this paper, we will study HH and validate it again...

Stochastic nature of earthquake has raised a challenge for engineers to choose which record for their analyses. Clustering is offered as a solution for such a data mining problem to automatically distinguish between ground motion records based on similarities in the corresponding seismic attributes. The present work formulates an optimization problem to seek for the best clustering measures. In...

Multidimensional scaling (MDS) which is a fundamental problem in data analysis, can be formulated as a nonlinear global optimization problem. In order to solve it, some local, heuristic and global optimization methods are implemented. In another hand, to perform our new heuristic approach, the MDS problem is studied by using a multistart Levenberg-Marquardt method and, also for some small examp...

The main aim of the present paper is to propose efficient multi-objective optimization algorithms (MOOAs) to tackle truss structure optimization problems. The proposed meta-heuristic algorithms are based on the firefly algorithm (FA) and bat algorithm (BA), which have been recently developed for single-objective optimization. In order to produce a well distributed Pareto front, some improvement...

This study deals with a new hybrid global–local optimization algorithm named PSOLVER that combines particle swarm optimization (PSO) and a spreadsheet ‘‘Solver” to solve continuous optimization problems. In the hybrid PSOLVER algorithm, PSO and Solver are used as the global and local optimizers, respectively. Thus, PSO and Solver work mutually by feeding each other in terms of initial and sub-i...

optimization is an important methodology for activities in planning and design. the transportation designers are able to introduce better projects when they can save time and cost of travel for project by optimization methods. most of the optimization problems in engineering are more complicated than they can be solved by custom optimization methods. the most common and available methods are he...

one of the most important extensions of the capacitated vehicle routing problem (cvrp) is the vehicle routing problem with simultaneous pickup and delivery (vrpspd) where customers require simultaneous delivery and pick-up service. in this paper, we propose an effective ant colony optimization (eaco) which includes insert, swap and 2-opt moves for solving vrpspd that is different with common an...

the travelling salesmen problem (tsp) is one of the most important and famous combinational optimization problems that aim to find the shortest tour. in this problem, the salesman starts to move from an arbitrary place called depot and after visiting all nodes, finally comes back to depot. solving this problem seems hard because program statement is simple and leads this problem belonging to np...