Two-level Evolutionary Algorithm Research Articles

A two-level evolutionary algorithm for dynamic scheduling in flexible job shop environment

A two-level evolutionary algorithm for dynamic scheduling in flexible job shop environment

Automatic optimal design of passive vibration control devices for buildings using two-level evolutionary algorithm

Automatic optimal design of passive vibration control devices for buildings using two-level evolutionary algorithm

Multi-objective evolutionary optimization for multi-period heat exchanger network retrofit

Increase in energy efficiency and reduction in greenhouse gas (GHG) emissions in industry are important steps towards a more sustainable economy. Due to the growing share of high value-added industries multi-period operation becomes more common in process industry. Therefore, retrofit of existing multi-period production plants is a key aspect towards more sustainable production processes. Hence, in this work, an existing two-level evolutionary algorithm using a genetic algorithm and a differential evolution for multi-period heat exchanger network retrofit is extended to consider GHG emissions as a second objective to the total annual cost (TAC). The multi-objective problem is addressed by incorporating a non-dominated sorting genetic algorithm (NSGA-II) and hypervolume indicators into the algorithm. By analyzing an industrial case study of a potato chips production, the results of the multi-objective optimization shows that GHG emissions can be reduced by 50%. However, compared to the single-objective optimization, TAC is increased by 27%. By selecting capital costs and operating costs as objectives instead, similar results to the single-objective optimization are achieved showing that the results are highly dependent on the selection of the objectives. Further, changes in utility costs and caused emissions have a high impact on the results.

A two-level evolutionary algorithm for solving the petrol station replenishment problem with periodicity constraints and service choice

This paper addresses the petrol station replenishment problem with periodicity constraints and introduces the frequency service choice as a decision variable. We present a mathematical optimization model for the problem and we develop first a simple heuristic method that is able to handle the complexity of the problem and then two metaheuristic approaches based on a novel two-level evolutionary algorithm. The first level deals with the periodicity and frequency selection of the visits to the petrol stations. The second level of evolution assigns the stations to the tank-trucks such that the total traveled distance is minimized. The effectiveness of the proposed approaches has been tested by means of a comprehensive experimental study by using first a set of randomly generated test cases and then a real-life problem.

Two-level evolutionary algorithm for discovering relations between nodes’ features in a complex network

Complex network theory offers an efficient mathematical framework for modelling natural phenomena. However, these studies focus mainly on the topological characteristics of networks, while the actual reasons behind the networks’ formation remain overlooked. This paper proposes a new approach to complex network analysis. By searching for the optimal functional definition of the network's edge set, it allows an examination of the influences of the physical properties of the nodes on the network's structure and behaviour (i.e. changes of the network's structure when the physical properties of nodes change). A two-level evolutionary algorithm is proposed for this purpose, whereby the search for a suitable function form is achieved at the first level, while the second level is used for optimal function fitting. In this way, not only the features with the largest influences are identified, but also the intensities of their influences are estimated. Synthetic networks are examined in order to show the superiority of the proposed approach over traditional machine learning algorithms, while the applicability of the proposed method is demonstrated on a real-world study of the behaviour of biological cells.

A two-level evolutionary algorithm for solving the facility location and design (1|1)-centroid problem on the plane with variable demand

In this work, the problem of a company or chain (the leader) that considers the reaction of a competitor chain (the follower) is studied. In particular, the leader wants to set up a single new facility in a planar market where similar facilities of the follower, and possibly of its own chain, are already present. The follower will react by locating another single facility after the leader locates its own facility. Both the location and the quality (representing design, quality of products, prices, etc.) of the new leader’s facility have to be found. The aim is to maximize the profit obtained by the leader considering the future follower’s entry. The demand is supposed to be concentrated at n demand points. Each demand point splits its buying power among the facilities proportionally to the attraction it feels for them. The attraction of a demand point for a facility depends on both the location and the quality of the facility. Usually, the demand is considered in the literature to be fixed or constant regardless the conditions of the market. In this paper, the demand varies depending on the attraction for the facilities. Taking variable demand into consideration makes the model more realistic. However, it increases the complexity of the problem and, therefore, the computational effort needed to solve it. Three heuristic methods are proposed to cope with this hard-to-solve global optimization problem, namely, a grid search procedure, a multistart algorithm and a two-level evolutionary algorithm. The computational studies show that the evolutionary algorithm is both the most robust algorithm and the one that provides the best results.

Metamodel-assisted evolutionary algorithms for the unit commitment problem with probabilistic outages

An efficient method for solving power generating unit commitment ( UC) problems with probabilistic unit outages is proposed. It is based on a two-level evolutionary algorithm ( EA) minimizing the expected total operating cost ( TOC) of a system of power generating units over a scheduling period, with known failure and repair rates of each unit. To compute the cost function value of each EA population member, namely a candidate UC schedule, a Monte Carlo simulation must be carried out. Some thousands of replicates are generated according to the units’ outage and repair rates and the corresponding probabilities. Each replicate is represented by a series of randomly generated availability and unavailability periods of time for each unit and the UC schedule under consideration accordingly. The expected TOC is the average of the TOCs of all Monte Carlo replicates. Therefore, the CPU cost per Monte Carlo evaluation increases noticeably and so does the CPU cost of running the EA. To reduce it, the use of a metamodel-assisted EA ( MAEA) with on-line trained surrogate evaluation models or metamodels (namely, radial-basis function networks) is proposed. A novelty of this method is that the metamodels are trained on a few “representative” unit outage scenarios selected among the Monte Carlo replicates generated once during the optimization and, then, used to predict the expected TOC. Based on this low cost, approximate pre-evaluation, only a few top individuals within each generation undergo Monte Carlo simulations. The proposed MAEA is demonstrated on test problems and shown to drastically reduce the CPU cost, compared to EAs which are exclusively based on Monte Carlo simulations.

A rewriting grammar for heat exchanger network structure evolution with stream splitting

The design of cost optimal heat exchanger networks is a difficult optimization problem owing both to the nonlinear models required and the combinatorial size of the search space. When stream splitting is considered, the combinatorial aspects make the problem even harder. This article describes the implementation of a two-level evolutionary algorithm based on a string rewriting grammar for the evolution of the heat exchanger network structure. A biological analogue of genotypes and phenotypes is used to describe structures and specific solutions, respectively. The top-level algorithm evolves structures while the lower level optimizes specific structures. The result is a hybrid optimization procedure that can identify the best structures including stream splitting. Case studies from the literature are presented to demonstrate the capabilities of the novel procedure.

A parallel global-local mixed evolutionary algorithm for multimodal function optimization based on domain decomposition

This paper presents a parallel two-level evolutionary algorithm based on domain decomposition for solving function optimization problem containing multiple solutions. By combining the characteristics of the global search and local search in each sub-domain, the former enables individual to draw closer to each optima and keeps the diversity of individuals, while the latter selects local optimal solutions known as latent solutions in sub-domain. In the end, by selecting the global optimal solutions from latent solutions in each sub-domain, we can discover all the optimal solutions easily and quickly.