the vehicle routing problem with simultaneous pickup and delivery (vrpspd) is a well-known combinatorial optimization problem which addresses provided service to a set of customers using a homogeneous fleet of capacitated vehicles. the objective is to minimize the distance traveled. the vrpspd is an np-hard combinatorial optimization problem. therefore, practical large-scale instances of vrpspd...

In this study an efficient meta-heuristic is proposed for layout optimization of truss structures by combining cellular automata (CA) and firefly algorithm (FA). In the proposed meta-heuristic, called here as cellular automata firefly algorithm (CAFA), a new equation is presented for position updating of fireflies based on the concept of CA. Two benchmark examples of truss structures are presen...

differential steering of in-wheel electric vehicle provides the functions of both active steering and power assisted steering with the coupling control of force and displacement transfer characteristic of system. a collaborative optimization model of the differential power-assisted steering system of in-wheel electric vehicle is built, with steering economy as the main system optimization goal,...

This study is investigated the optimum parameters for a tuned mass damper (TMD) under the seismic excitation. Shuffled complex evolution (SCE) is a meta-heuristic optimization method which is used to find the optimum damping and tuning frequency ratio for a TMD. The efficiency of the TMD is evaluated by decreasing the structural displacement dynamic magnification factor (DDMF) and acceleration ...

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 funct...

Opposition-based learning was first introduced as a solution for machine learning; however, it is being extended to other artificial intelligence and soft computing fields including meta-heuristic optimization. It not only utilizes an estimate of a solution but also enters its counter-part information into the search process. The present work applies such an approach to Colliding Bodies Optimiz...

gravitational search algorithm (gsa) is one of the newest swarm based optimization algorithms, which has been inspired by the newtonian laws of gravity and motion. gsa has empirically shown to be an efficient and robust stochastic search algorithm. since introducing gsa a convergence analysis of this algorithm has not yet been developed. this paper introduces the first attempt to a formal conve...