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The objective of the research done in this paper was to determine cost of the power and heat system with pressurized fluidized bed gasifier using exergoexonomic appraisal techniques. Exergetic efficiency maximization was approached with use of multi-objective evolutionary optimization methods which were designed such that the costs were minimized in line with the exergoeconomic plans. The results...
An Optimal Control is a set of differential equations describing the path of the control variables that minimize the cost functional (function of both state and control variables). Direct solution methods for optimal control problems treat them from the perspective of global optimization: perform a global search for the control function that optimizes the required objective. Invasive Weed Optimization...
In this paper, we show the effectiveness of an EMO (Evolutionary Multi-criterion Optimization) algorithm with objective reduction using a correlation-based weighted-sum in many objective knapsack problems. Recently many EMO algorithms are proposed for various multi-objective problems. However, it is known that the convergence performance to the Pareto-frontier becomes weak in approaches using archives...
In this paper the short-term hydro-thermal scheduling problem is solved using a modified bacterial foraging algorithm (MBFA). The integrated hydro-thermal systems considered include fixed-head hydro reservoirs. The short-term hydro-thermal scheduling (STHTS) problem is a dynamic large-scale nonlinear optimization problem which requires solving unit commitment and economic power load dispatch problems...
Phase problems arise from lost phase information in measurement of diffraction waves. The missing phase should be retrieved to reconstruct an object image from the diffraction pattern. This paper proposes a hybrid type approach, Evolutionary-based GS (E-GS), based on the Gerchberg - Saxton algorithm (GS algorithm) and Evolutionary Multicriterion Optimisation (EMO). There are three main aims of E-GS:...
Evolutionary algorithms are modified in various ways to solve constrained optimization problems. Of them, the use of a bi-objective evolutionary algorithm in which the minimization of the constraint violation is included as an additional objective, has received a significant attention. Classical penalty function approach is another common methodology which requires an appropriate knowledge of the...
In real life optimization problems, it is very important to have high quality solutions (optimal). But when uncertainty becomes part of the optimization problem, solutions should be optimal and robust to the uncertain environmental changes. This paper focuses on finding robust optimal solution for the vehicle routing problem with stochastic demands VRPSD. In this case when the uncertainty of the customers...
The design and application of termination criteria has become an important aspect in evolutionary multi-objective optimization. Online convergence detection (OCD) determines when further generations are no longer promising based on statistical tests on a set of performance indicators. The behavior of OCD mainly depends on two parameters, the number of preceding generations considered in the statistical...
This study proposes a method that creates a scent suited with a user's favor using paired comparison-based Interactive Differential Evolution. In the proposed method, the user smells two scents and selects the preferred one by simple comparison. Based on the repetitive comparisons, Differential Evolution (DE) optimizes the scent suited with the user. Each scent is composed of several source scents,...
In this paper, the Multiple Trajectory Search (MTS) is presented for single objective constrained real-parameter optimization problems. The MTS uses multiple agents to search the solution space concurrently. Each agent does an iterated region search using one of three candidate region search methods. By choosing a region search method that best fits the landscape of a solution's neighborhood, an agent...
In this work we present a novel progress indicator, called fitness homogeneity indicator (FHI). This indicator improves the other previously discussed indicators as it takes into account all possible processes taking place in the population while not requiring an intensive computation as it relies on the fitness values calculated for the individuals. It is also capable of equally detecting success...
Real-world design optimization problems are typically computationally-expensive and to address this various model-assisted evolutionary frameworks have been proposed. However, often such problems are also high-dimensional and in such settings models tend to have poor accuracy and thus degrade the optimization search. To address this we propose two complementary dimensionality-reduction frameworks...
A key parameter affecting the operation of differential evolution (DE) is the crossover rate Cr ϵ [0, 1]. While very low values are recommended for and used with separable problems, on non-separable problems, which include most real-world problems, Cr = 0.9 has become the de facto standard, working well across a large range of problem domains. Recent work on separable and non-separable problems has...
This paper presents a study for using Kriging metamodeling in combination with Covariance Matrix Adaptation Evolution Strategies (CMA-ES) to find robust solutions. A general, archive based, framework is proposed for integrating Kriging within CMA-ES, including a method to utilize the covariance matrix of the CMA-ES in a straightforward way to improve the accuracy of the Kriging predictions without...
In this paper we describe an improved version of self-adaptive differential evolution algorithm. Our algorithm uses more strategies, ageing mechanism to reinitialize an individual which stagnates in local optima, an ϵ level controlling of constraint violation. The performance of the proposed algorithm is evaluated on the set of 18 scalable benchmark functions provided for the CEC 2010 competition...
Lattice-bound folding models are often used by computer scientists as a simplified instance of the chain folding problem (featuring, at the other end of the complexity spectrum, conformational sampling of biologically important molecules). Lattice-bound folding is relatively fast, thus well suited for benchmarking of nature-inspired optimization heuristics. Yet, it is not clear whether the benchmark-winning...
Inspired by political parties' behavior in parliament's elections of chairman, Parliameantary Optimization Algorithm (POA) has emerged as a new stochastic population-based optimizer. Current research has proven POA efficiency in numerical optimization but it is difficult to find a POA version that deals with combinatorial optimization. In this paper we present a parliamentary algorithm that can solve...
Invasive Weed Optimization (IWO) is a recently developed derivative-free metaheuristic algorithm that mimics the robust process of weeds colonization and distribution in an ecosystem. On the other hand central to an ecosystem is the foraging behavior that pertains to the act of searching for food and forms an integral part of the daily life of most of the living creatures. For over past two decades,...
Economic Load Dispatch (ELD) optimization is an important and difficult task in power system planning. Previously, most of the research mainly focused on proposing various evolutionary algorithms (EAs) to pursue better results of ELD problems. However, few comprehensive analysis of the effects of various constraint handling techniques (CHTs) on the performance of EA-based techniques are available...
In this paper the optimization of a Tubular Permanent Magnet-Linear Generator (TPM-LiG) for energy generation is presented. The application is related to the sea wave energy generation for small sensorized buoy. The optimization process is developed by means of an hybrid evolutionary algorithm widely presented in the paper. The advantage of this algorithm is in the wide exploration of the variables...
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