Structural optimization, when approached by conventional (gradient based) minimization algorithms presents several difficulties, mainly related to computational aspects for the huge number of nonlinear analyses required, that regard both Objective Functions (OFs) and Constraints. Moreover, from the early '80s to today's, Evolutionary Algorithms have been successfully developed and applied as a ...

Airline crew pairing problems involve optimizing an overall evaluation function containing various conflicting objectives and constraints originating from cost and safety considerations. Classical approaches based on set partitioning or set covering methods separate the solution into two phases, pairing generation and pairing optimization, and evaluate the cost by a weighted-sum of objective va...

Structural  design  optimization  usually  deals  with  multiple  conflicting  objectives  to  obtain the minimum construction cost, minimum weight, and maximum safety of the final design. Therefore, finding the optimum design is hard and time-consuming for  such problems.  In this paper, we borrow the basic concept of multi-criterion decision-making and combine it with  Particle  Swarm  Optimi...

Water resources planning and management require problem resolution and optimized use of resources. Since many objectives in water management are conflicting, it is hard to devise one optimum strategy. A simulation tool capable of optimized multiobjective analysis to satisfy multiplicity of goals is needed to support water decision making. This paper suggests an integrated modeling framework to ...

Many real-world applications involve dealing with several conflicting objectives which need to be optimized simultaneously. Moreover, these problems may require the consideration of limitations that restrict their decision variable space. Evolutionary Algorithms (EAs) are capable tackling Multi-objective Optimization Problems (MOPs). However, approaches struggle accurately approximate a feasibl...