This paper proposes a novel metaheuristic framework using a Differential Evolution (DE) algorithm with the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). Both algorithms are combined employing a collaborative strategy with sequential execution, which is called DE-NSGA-II. The DE-NSGA-II takes advantage of the exploration abilities of the multi-objective evolutionary algorithms strengthened with the ability to search global mono-objective optimum of DE, that enhances the capability of finding those extreme solutions of Pareto Optimal Front (POF) difficult to achieve. Numerous experiments and performance comparisons between different evolutionary algorithms were performed on a referent problem for the mono-objective and multi-objective literature, which consists of the design of a double reduction gear train. A preliminary study of the problem, solved in an exhaustive way, discovers the low density of solutions in the vicinity of the optimal solution (mono-objective case) as well as in some areas of the POF of potential interest to a decision maker (multi-objective case). This characteristic of the problem would explain the considerable difficulties for its resolution when exact methods and/or metaheuristics are used, especially in the multi-objective case. However, the DE-NSGA-II framework exceeds these difficulties and obtains the whole POF which significantly improves the few previous multi-objective studies.

The continuous evolution of materials and technologies of additive manufacturing has led to a competitive production process even for functional parts. The capabilities of these technologies for manufacturing complex geometries allow the definition of new designs that cannot be obtained with any other manufacturing processes. An application where this capability can be exploited is the lightening of parts using internal structures. This allows to obtain more efficient parts and, at the same time, reduce the costs of material and manufacturing time. A new lightweight optimisation method to optimise the design of these structures and minimise weight while keeping the minimal mechanical properties is presented in this paper. This method is based on genetic algorithms, metamodels and finite element analysis (FEA). This combination reduces the number of FEA simulations required during the optimisation process, thereby reducing the design time. This methodology is experimentally applied to a reference geometry oriented both for selective laser sintering (SLS) and Polyjet technologies. In both cases, an optimised and a non-optimised design are manufactured and tested in order to experimentally compare the stiffness results between them. The optimum design achieved a specific stiffness 72.82% higher than the non-optimised design in the SLS case study, and 3.14 times higher in the Polyjet case study.

Few activities are as crucial in urban environments as waste management. Mismanagement of waste can cause significant economic, social, and environmental damage. However, waste management is often a complex system to manage and therefore where computational decision-support tools can play a pivotal role in assisting managers to make faster and better decisions. In this sense, this article proposes, on the one hand, a unified optimization model to address two common waste management system optimization problem: the determination of the capacity of waste bins in the collection network and the design and scheduling of collection routes. The integration of these two problems is not usual in the literature since each of them separately is already a major computational challenge. Two improved exact formulations based on mathematical programming and two metaheuristic methods are provided to solve this proposed unified optimization model. It should be noted that the metaheuristics consider a mixed chromosome representation of the solutions combining binary and integer alleles, in order to solve realistic instances of this complex problem. Different parameters of the metaheuristics considered – a Genetic Algorithm and a Simulated Annealing algorithm – have been tested to study which combination of them obtained better results in execution times on the order of that of the exact solvers. The achieved results show that the proposed metaheuristic methods perform efficient on large instances, where exact formulations are not applicable, and offer feasible, high-quality solutions in reasonable calculation times.