A generic integrated configuration-size optimisation formulation for design of hybrid renewable energy systems (HRES) is presented in this paper. This allows identifying the optimum configuration a given site and size each component that by solving only one problem. Single multiobjective case studies are defined both on-grid standalone using wind turbine, PV panel, battery bank, fuel cell, elec...

The paper describes a multiobjective optimization study for industrial styrene reactors using non-dominated sorting genetic algorithm (NSGA). Several twoand threeobjective functions, namely, production, yield and selectivity of styrene, are considered for adiabatic as well as steam-injected styrene reactors. Pareto optimal (a set of equally good) solutions are obtained due to conflicting effect...

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 ...

In this paper, a bi-objective multi-product (r,Q) inventory model in which the inventory level is reviewed continuously is proposed. The aim of this work is to find the optimal value for both order quantity and reorder point through minimizing the total cost and maximizing the service level of the proposed model simultaneously. It is assumed that shortage could occur and unsatisfied demand coul...

In this paper, design-oriented field effect transistor (FET) models are produced. For this purpose, FET modeling is put forward as a constrained, multiobjective optimization problem. Two novel methods for multiobjective optimization are employed: particle swarm optimization (PSO) uses the single-objective function, which gathers all of the objectives as aggregating functions; and the nondominat...

Previous research has shown that sub-population genetic algorithm is effective in solving the multiobjective combinatorial problems. Based on these pioneering efforts, this paper extends the SPGA algorithm with a global Pareto archive technique and a two-stage approach to solve the multi-objective problems. In the first stage, the areas next to the two single objectives are searched and solutio...

The fruit fly optimization algorithm (FOA) is a global optimization algorithm inspired by the foraging behavior of a fruit fly swarm. In this study, a novel stochastic fractal model based fruit fly optimization algorithm is proposed for multiobjective optimization. A food source generating method based on a stochastic fractal with an adaptive parameter updating strategy is introduced to improve...

The nondominated sorting genetic algorithm (NSGA) is adapted and used to obtain multiobjective Pareto optimal solutions for three grades of nylon 6 being produced in an industrial semibatch reactor. The total reaction time and the concentration of an undesirable cyclic dimer in the product are taken as two individual objectives for minimization, while simultaneously requiring the attainment of ...

Many algorithms for multiobjective optimization have been proposed in the last years. In the recent past a great importance have the MOEAs able to solve problems with more than two objectives and with a large number of decision vectors (space dimensions). The difficulties occur when problems with more than three objectives (higher dimensional problems) are considered. In this paper, a new algor...