This study examines the robust facility layout problem (RFLP) while taking into account unpredictable health and environmental safety standards. problem's major goal is to arrange departments in various of a hall, allot each department appropriate amount space, identify kind amenities equipment needed for chosen sector. To accomplish aforementioned objective, five criteria were taken account: t...

During the last few years, multi-objective optimization processes have become one of main challenges for energy efficiency in buildings. In this work, a new efficient method, based on Building Performance Optimization (BPO) technique, has been developed to improve indoor thermal comfort and performance residential buildings, i.e. Moroccan ground floor + first (GFFF) house located Marrakech regi...

despite the growing use of evolutionary multi-objective optimization algorithms in different categories of science, these algorithms as a powerful tool in portfolio optimization and specially solving multi-objective portfolio optimization problem is still in its early stages. in this paper, moeas have been used for solving multi-objective portfolio optimization problem in tehran stock market. f...

In this chapter, we present a multi-objective evolutionary algorithm (MOEA) based on the heuristic called “particle swarm optimization” (PSO). This multi-objective particle swarm optimizer (MOPSO) is characterized for using a very small population size, which allows it to require a very low number of objective function evaluations (only 3000 per run) to produce reasonably good approximations of...

In this paper, a new dynamic self-adaptive multi-objective particle swarm optimization (DSAMOPSO) method is proposed to solve binary-state multi-objective reliability redundancy allocation problems (MORAPs). A combination of penalty function and modification strategies is used to handle the constraints in the MORAPs. A dynamic self-adaptive penalty function strategy is utilized to handle the co...

Constraint handling techniques are mainly designed for evolutionary algorithms to solve constrained multiobjective optimization problems (CMOPs). Most multiojective particle swarm optimization (MOPSO) designs adopt these existing constraint handling techniques to deal with CMOPs. In the proposed constrained MOPSO, information related to particles’ infeasibility and feasibility status is utilize...

Proteins interact with each other in a highly specific manner, and protein interactions play a key role in many cellular processes. Since protein interactions determine the outcome of most cellular processes, so identifying and characterizing Protein– Protein interactions and their networks are essential for understanding the mechanisms of biological processes on a molecular level. This paper e...

One of the major distinguishing features of the dynamic multiobjective optimization problems (DMOPs) is the optimization objectives will change over time, thus tracking the varying Pareto-optimal front becomes a challenge. One of the promising solutions is reusing the “experiences” to construct a prediction model via statistical machine learning approaches. However most of the existing methods ...

This paper proposes an integrated approach to arrive at optimal build orientations, simultaneously minimizing surface roughness ’Ra’ and build time ’T ’, for object manufacturing in SLS process. The optimization task is carried out by two popularly known multi-objective evolutionary optimizers NSGA-II (non-dominated sorting genetic algorithm) and MOPSO (multi-objective particle swarm optimizer)...