Scalarizing Functions in Decomposition-Based Multiobjective Evolutionary Algorithms

Decomposition-based multiobjective evolutionary algorithms (MOEAs) have received increasing research interests due to their high performance for solving multiobjective optimization problems. However, scalarizing functions (SFs), which play a crucial role in balancing diversity and convergence in these kinds of algorithms, have not been fully investigated. This paper is mainly devoted to presenting two new SFs and analyzing their effect in decomposition-based MOEAs. Additionally, we come up with an efficient framework for decomposition-based MOEAs based on the proposed SFs and some new strategies. Extensive experimental studies have demonstrated the effectiveness of the proposed SFs and algorithm

Knowledge transfer with mixture model in dynamic multi-objective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Most existing dynamic multi-objective evolutionary algorithms (DMOEAs) have been designed to handle dynamic multi-objective optimization problems (DMOPs) with regular environmental changes. However, they often overlook scenarios where environmental changes are irregular and less predictable. Recently, knowledge transfer has been proposed as a novel paradigm for solving DMOPs. Despite this, most transfer strategies only consider transferring knowledge obtained from the previous environment while ignoring significant differences that may exist between adjacent environments due to irregular changes. To address these issues, this paper proposes a novel knowledge transfer strategy based on a Gaussian mixture model (denoted as KTMM) for solving DMOPs with irregular changes. In particular, an adaptive Gaussian mixture model is designed to capture the knowledge of historical environments, which is then transferred to generate an initial population for the new environment. Additionally, a new method for controlling irregular changes is introduced into widely-used benchmarks to form the DMOP benchmark with irregular changes. Our proposed KTMM is compared with six state-of-the-art DMOEAs on several benchmark problems with irregular changes. Experimental results demonstrate the superiority of our proposed method in most test instances and in a real-world problem

Empirical Evidence on Inflation and Unemployment in the Long Run

We examine the relationship between inflation and unemployment in the long run, using quarterly US data from 1952 to 2010. Using a band-pass filter approach, we find strong evidence that a positive relationship exists, where inflation leads unemployment by some 3 to 3 1/2 years, in cycles that last from 8 to 25 or 50 years. Our statistical approach is atheoretical in nature, but provides evidence in accordance with the predictions of Friedman (1977) and the recent New Monetarist model of Berentsen, Menzio, and Wright (2011): the relationship between inflation and unemployment is positive in the long run

Currency Areas and Monetary Coordination ∗

In this paper we examine currency areas and monetary policy coordination in a two-country model where the value of each country’s currency is determined endogenously. Exchanges in the goods markets are modelled as random bilateral matches, and households choose the amount and the frequency of purchases made in each currency area. By contrast, the currency market is centralized and Walrasian. We determine the nominal exchange rate in the equilibrium and show that the size of a currency area decreases with the growth rate of that currency and increases with the growth rate of the competing currency. We also find the following welfare results on monetary competition and coordination. First, policy coordination results in the Friedman rule being chosen for each currency. Second, policy competition increases inflation and reduces welfare in both countries relative to policy coordination. Third, under policy competition, the incentive to inflate increases with the degree of the integration of the goods markets. Fourth, currency unification delivers the same allocation as policy coordination, provided that the latter can be achieved. Preliminary. Do not quote. Please send comments to eithe

Currency Areas and Monetary Coordination

Currency Area; Monetary Coordination

Design of the MVT RBF neural network robotic manipulator control system based on model block approximation

Due to the uncertain dynamic characteristics, the requirements for robotic manipulator control are increasingly complex. The traditional radial basis function (RBF) neural network has a good generalization ability, but its redundant and tedious training process cannot meet the “Intelligent” control requirement of robotic manipulator. This study designs a new valve-regulated memristive RBF neural network, which adopts the model block approximation control strategy to estimate the three coefficient matrices of the robotic manipulator and uses the memristor with voltage threshold (MVT) as an electronic synapse to provide connections between neurons for the neural network and store information. This study adopts the design idea of software hardening and replaces the updated neural network weight with the change of the memristance value in the MVT network (crossed array), which can effectively improve the control performance of the traditional RBF neural network and can also provide analytical data for the fault detection of the subsequent control system. A simulation analysis is conducted with a single-joint robotic manipulator as the control object, and the results verify the rationality and feasibility of the proposed control algorithm. </jats:p