Multi-objective optimization methods are essential to resolve real-world problems as most involve several types of objects. Several multi-objective genetic algorithms have been proposed. Among them, SPEA2 and NSGA-II are the most successful. In the present study, two new mechanisms were added to SPEA2 to improve its searching ability a more effective crossover mechanism and an archive mechanism...

This chapter introduces two algorithms for multiobjective optimization. These algorithms are based on a state-of-the-art Multiobjective Evolutionary Algorithm (MOEA) called Strength Pareto Evolutionary Algorithm 2 (SPEA2). The first proposed algorithm implements a competitive coevolution technique within SPEA2. In contrast, the second algorithm introduces a cooperative coevolution technique to ...

In this paper, Dual-Archive scheme (DA scheme) for Multi objective Genetic Algorithms is proposed. The DA scheme is the mechanism to maintain the diversity of the solutions of Multi objective Genetic Algorithms in both objective space and design variable space.When decision makers choose the solution from the Pareto solutions, they use not only the objective value information but also the desig...

In order to help the forging enterprise realize energy conservation and emission reduction, the scheduling problem of furnace heating was improved in this paper. Aiming at the charging problem of continuous heating furnace, a multi-objective furnace charging model with minimum capacity difference and waiting time was established in this paper. An improved strength Pareto evolutionary algorithm ...

This study provides a comprehensive assessment of state-of-the-art evolutionary multiobjective optimization (EMO) tools’ relative effectiveness in calibrating hydrologic models. The relative computational efficiency, accuracy, and ease-of-use of the following EMO algorithms are tested: Epsilon Dominance Nondominated Sorted Genetic AlgorithmII (ε-NSGAII), the Multiobjective Shuffled Complex Evol...

Several problems in the area of financial optimization can be naturally dealt with optimization techniques under multiobjective approaches, followed by a decision-making procedure on the resulting efficient solutions. The problem of portfolio optimization is one of them. This chapter studies the use of evolutionary multiobjective techniques to solve such problems, focusing on Venezuelan market ...

Effective multiobjective hydrologic model calibration P. Reed et al. Papers published in Hydrology and Earth System Sciences Discussions are under open-access review for the journal Hydrology and Earth System Sciences Effective multiobjective hydrologic model calibration P. Reed et al. Abstract This study provides a comprehensive assessment of state-of-the-art evolutionary multi-objective optim...

In this paper, we present performance comparisons between two popular elitism–based evolutionary multi-objective optimization algorithms -NSGA2 and SPEA2 in the presence of noise. Three test problems and six noise levels are employed in the research experiments. The results show that SPEA2 outperforms NSGA2 in the early generations. NSGA2, however, is superior during latter generations regardle...

⎯ The integrated control of voltage and reactive power (volt/var) on radial distribution feeder is formulated as a multiobjective optimization problem to be solved trough the Strength Pareto Evolutionary Algorithm (SPEA2), a relatively recent technique of recognized computational efficiency. Two objectives has been established: voltage level variation and power and energy losses including costs...

The following MOEA algorithms are briefly summarized and compared: • NPGA Niched Pareto Genetic Algorithm (1994) – NPGA II (2001) • NSGA Non-dominated Sorting Genetic Algorithm (1994) – NSGA II (2000) • SPEA Strength Pareto Evolutionary Algorithm (1998) – SPEA2 (2001) – SPEA2+ (2004) – ISPEA Immunity SPEA (2003) • PAES Pareto Archived Evolution Strategy (2000) – M-PAES Mimetic PAES (2000) • PES...