Multi-objective optimization is the simultaneous consideration of two or more objective functions that are completely or partially inconflict with each other. The optimality of such optimizations is largely defined through the Pareto optimality. Multiple objective integer linear programs (MOILP) are special cases of multiple criteria decision making problems. Numerous algorithms have been desig...

A tremendous amount of work has been done in the recent years optimization input parameters, however, current techniques can only provide a single optimal process parameter combination. Although alternative have developed to multiple solutions with identical objective values, these low efficiency when searching for solutions. In this paper, two-stage filter split-optimization approach is propos...

While evolutionary computing inspired approaches to multi-objective optimization have many advantages over conventional approaches; they generally do not explicitly exploit directional/gradient information. This can be inefficient if the underlying objectives are reasonably smooth, and this may limit the application of such approaches to real-world problems. This paper develops a local framewor...

there have been many productive methods developed so far for optimization of multiple response surface (mrs) problems. this paper tends to review the most seminal approaches in mrs and discuss the strength and weakness of each of the approaches through existing aspects in mrs. a numerical example is included to compare results by different methods. finally some of the prominent areas for future...