Multi-population inflationary differential evolution algorithm with adaptive local restart

In this paper a Multi-Population Inflationary Differential Evolution algorithm with Adaptive Local Restart is presented and extensively tested over more than fifty test functions from the CEC 2005, CEC 2011 and CEC 2014 competitions. The algorithm combines a multi-population adaptive Differential Evolution with local search and local and global restart procedures. The proposed algorithm implements a simple but effective mechanism to avoid multiple detections of the same local minima. The novel mechanism allows the algorithm to decide whether to start or not a local search. The local restart of the population, which follows the local search, is, therefore, automatically adapted

Differential evolution to solve the lot size problem.

An Advanced Resource Planning model is presented to support optimal lot size decisions for performance improvement of a production system in terms of either delivery time or setup related costs. Based on a queueing network, a model is developed for a mix of multiple products following their own specific sequence of operations on one or more resources, while taking into account various sources of uncertainty, both in demand as well as in production characteristics. In addition, the model includes the impact of parallel servers and different time schedules in a multi-period planning setting. The corrupting influence of variabilities from rework and breakdown is explicitly modeled. As a major result, the differential evolution algorithm is able to find the optimal lead time as a function of the lot size. In this way, we add a conclusion on the debate on the convexity between lot size and lead time in a complex production environment. We show that differential evolution outperforms a steepest descent method in the search for the global optimal lot size. For problems of realistic size, we propose appropriate control parameters for the differential evolution in order to make its search process more efficient.Production planning; Lot sizing; Queueing networks; Differential evolution;

Deep Reinforcement Learning for Adaptive Parameter Control in Differential Evolution for Multi-Objective Optimization

Evolutionary algorithms (EA) are efficient population-based stochastic algorithms for solving optimization problems. The performance of EAs largely depends on the configuration of values of parameters that control their search. Previous works studied how to configure EAs, though, there is a lack of a general approach to effectively tune EAs. To fill this gap, this paper presents a consistent, automated approach for tuning and controlling parameterized search of an EA. For this, we propose a deep reinforcement learning (DRL) based approach called ‘DRL-APC-DE’ for online controlling search parameter values for a multi-objective Differential Evolution algorithm. The proposed method is trained and evaluated on widely adopted multi-objective test problems. The experimental results show that the proposed approach performs competitively to a non-adaptive Differential Evolution algorithm, tuned by grid search on the same range of possible parameter values. Subsequently, the trained algorithms have been applied to unseen multi-objective problems for the adaptive control of parameters. Results show the successful ability of DRL-APC-DE to control parameters for solving these problems, which has the potential to significantly reduce the dependency on parameter tuning for the successful application of EAs

Non-dominated sorting gravitational search algorithm for multi-objective optimization of power transformer design

Transformers are crucial components in power systems. Due to market globalization, power transformer manufacturers are facing an increasingly competitive environment that mandates the adoption of design strategies yielding better performance at lower mass and losses. Multi-objective Optimization Problems (MOPs) consist of several competing and incommensurable objective functions. Recently, as a search optimization technique inspired by nature, evolutionary algorithms have been broadly applied to solve MOPs. In this paper, a power Transformer Design (TD) methodology using Non-dominated Sorting Gravitational Search Algorithm (NSGSA) is proposed. Results are obtained and presented for NSGSA approach. The obtained results for the study case are compared with those results obtained when using other multi objective optimization algorithms which are Novel Gamma Differential Evolution (NGDE) Algorithm, Chaotic Multi-Objective Algorithm (CMOA), and Multi- Objective Harmony Search (MOHS) algorithm. From the analysis of the obtained results, it has been concluded that NSGSA algorithm provides the most optimum solution and the best results in terms of normalized arithmetic mean value of two objective functions using NSGSA to the TD optimization

A Multi-Objective Memetic Optimization Method for Power Network Cascading Failures Protection

International audienceReliable and safe power grid operation requires the anticipation of cascading failures and the establishment of appropriate protection plans for their management. In this paper, we address this latter problem by line switching and propose a multi-objective memetic algorithm (MOMA), which combines the binary differential evolution algorithm with the non-dominated sorting mechanism and the Lamarckian local search. The 380 kV Italian power transmission network is used as a realistic test case

Adaptive dynamic disturbance strategy for differential evolution algorithm

To overcome the problems of slow convergence speed, premature convergence leading to local optimization and parameter constraints when solving high-dimensional multi-modal optimization problems, an adaptive dynamic disturbance strategy for differential evolution algorithm (ADDSDE) is proposed. Firstly, this entails using the chaos mapping strategy to initialize the population to increase population diversity, and secondly, a new weighted mutation operator is designed to weigh and combinemutation strategies of the standard differential evolution (DE). The scaling factor and crossover probability are adaptively adjusted to dynamically balance the global search ability and local exploration ability. Finally, a Gauss perturbation operator is introduced to generate a random disturbance variation, and to accelerate premature individuals to jump out of local optimization. The algorithm runs independently on five benchmark functions 20 times, and the results show that the ADDSDE algorithm has better global optimization search ability, faster convergence speed and higher accuracy and stability compared with other optimization algorithms, which provide assistance insolving high-dimensionaland complex problems in engineering and information science

Differential Evolution Methods for the Fuzzy Extension of Functions

The paper illustrates a differential evolution (DE) algorithm to calculate the level-cuts of the fuzzy extension of a multidimensional real valued function to fuzzy numbers. The method decomposes the fuzzy extension engine into a set of "nested" min and max box-constrained op- timization problems and uses a form of the DE algorithm, based on multi populations which cooperate during the search phase and specialize, a part of the populations to find the the global min (corresponding to lower branch of the fuzzy extension) and a part of the populations to find the global max (corresponding to the upper branch), both gaining efficiency from the work done for a level-cut to the subsequent ones. A special ver- sion of the algorithm is designed to the case of differentiable functions, for which a representation of the fuzzy numbers is used to improve ef- ficiency and quality of calculations. The included computational results indicate that the DE method is a promising tool as its computational complexity grows on average superlinearly (of degree less than 1.5) in the number of variables of the function to be extended.Fuzzy Sets, Differential Evolution Method, Fuzzy Extension of Functions

Minimization of Active Power Loss and Voltage Profile Fortification by Using Differential Evolution – Harmony Search Algorithm

This paper presents DEHS (Differential Evolution-harmony Search) algorithm for solving the multi-objective reactive power dispatch problem .Harmony Search is a new heuristic algorithm, which mimics the procedure of a music player to search for an ideal state of harmony in music playing. Harmony Search can autonomously mull over each component variable in a vector while it generates a new vector. These features augment the flexibility of the Harmony Search algorithm and produce better solutions and overcome the disadvantage of Differential Evolution. Improved Differential Evolution method based on the Harmony Search Scheme, which we named it DEHS (Differential Evolution-harmony Search). The DEHS method has two behaviors. On the one hand, DEHS has the flexibility. It can adjust the values lightly in order to get a better global value for optimization. On the other hand,   DEHS can greatly boost the population’s diversity. It not only uses the DE’s strategies to search for global optimal results, but also utilize HS’s tricks that generate a new vector by selecting the components of different vectors randomly in the harmony memory and its outside. In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and compared to other algorithms.