Evolutionary Algorithms have been applied to single and multiple objectives optimization problems, with a strong emphasis on problems, solved through numerical simulations. However in several engineering problems, there is limited availability of suitable models and there is need for optimization of realistic or experimental configurations. The multiobjective optimization of an experimental set...

Evolutionary algorithms have (EAs) been an alternative class of powerful search techniques. They have been widely applied to solve multi-objective optimization problems from scientific community and engineering fields. The aim of designing EAs for multi-objective optimization is to obtain a well-converged and well-distributed set involving multiple Pareto-optimal solutions in a single simulatio...

The traffic signal design problem is a typical multiobjective optimization problem. In this research, a simulation-based multiobjective evolutionary approach for traffic signal design problems is proposed. This approach has a robust advantage that the numerical simulators and the optimization solvers can be changed and used easily for other applications. In this research, it combined the NSGA-I...

Many decision making problems can be formulated as multi-objective optimization problems (MOP). There hardly exists the solution that optimizes all objective functions in MOP, and then the concept of Pareto optimal solution (or efficient solution) is introduced. Usually, there exist a lot of Pareto optimal solutions, which are considered as candidates of final decision making solution. It is an...

Obtaining a fullest possible representation of solutions to a multiobjective optimization problem has been a major concern in Multi-Objective Genetic Algorithms (MOGAs). This is because a MOGA, due to its very nature, can only produce a discrete representation of Pareto solutions to a multiobjective optimization problem that usually tend to group into clusters. This paper presents a new MOGA, o...

Safety risk management has a considerable effect on disproportionate injury rate of construction industry, project cost and both labor and public morale. On the other hand time-cost optimization (TCO) may earn a big profit for project stakeholders. This paper has addressed these issues to present a multi-objective optimization model to simultaneously optimize total time, total cost and overall ...

Efficient control techniques must be preceded by well-designed processes. A generally accepted definition of a well-designed process is one that is Pareto optimal, i.e., no design objective can be improved without degrading at least one other design objective. Indeed, optimal design enables effective trade-off of competing design objectives, including controllability and robustness goals. The m...

Abstract: In multi-objective optimization problems, the optimization target is to obtain a set of non-dominated solutions. Comparing solution sets is crucial in evaluating the performances of different optimization algorithms. The use of performance indicators is common in comparing those sets and, subsequently, optimization algorithms. A good solution set must be close to the Pareto-optimal fr...

Most of the engineering problems are modeled as evolutionary multiobjective optimization problems, but they always ask for only one best solution, not a set of Pareto optimal solutions. The decision maker’s subjective information plays an important role in choosing the best solution from several Pareto optimal solutions. Generally, the decision-making processing is implemented after Pareto opti...

A newmultiobjective particle swarm optimization (MOPSO) technique for environmental/economic dispatch (EED) problem is proposed in this paper. The proposed MOPSO technique evolves a multiobjective version of PSO by proposing redefinition of global best and local best individuals in multiobjective optimization domain. The proposedMOPSO technique has been implemented to solve the EED problemwith ...