Inherent part of evolutionary algorithms that are based on Darwin's theory of evolution and Mendel's theory of genetic heritage, are random processes since genetic algorithms and evolutionary strategies are us...
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Inherent part of evolutionary algorithms that are based on Darwin's theory of evolution and Mendel's theory of genetic heritage, are random processes since genetic algorithms and evolutionary strategies are used. In this paper, we present extended experiments (of our previous) of selected evolutionary algorithms and test functions showing whether random processes really are needed in evolutionary algorithms. In our experiments we used differential evolution and SOMA algorithms with functions 2ndDeJong, Ackley, Griewangk, Rastrigin, SineWave and StretchedSineWave. We use n periodical deterministic processes (based on deterministic chaos principles) instead of pseudo-random number generators (PRGNs) and compare performance of evolutionary algorithms powered by those processes and by PRGNs. Results presented here are numerical demonstrations rather than mathematical proofs. We propose the hypothesis that a certain class of deterministic processes can be used instead of PRGNs without lowering the performance of evolutionary algorithms.
A recent comparison of well-established multiobjective evolutionary algorithms (MOEAs) has helped better identify the current state-of-the-art by considering (i) parameter tuning through automatic configuration, (ii) ...
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A recent comparison of well-established multiobjective evolutionary algorithms (MOEAs) has helped better identify the current state-of-the-art by considering (i) parameter tuning through automatic configuration, (ii) a wide range of different setups, and (iii) various performance metrics. Here, we automatically devise MOEAs with verified state-of-the-art performance for multi- and many-objective continuous optimization. Our work is based on two main considerations. The first is that high-performing algorithms can be obtained from a configurable algorithmic framework in an automated way. The second is that multiple performance metrics may be required to guide this automatic design process. In the first part of this work, we extend our previously proposed algorithmic framework, increasing the number of MOEAs, underlying evolutionary algorithms, and search paradigms that it comprises. These components can be combined following a general MOEA template, and an automatic configuration method is used to instantiate high-performing MOEA designs that optimize a given performance metric and present state-of-the-art performance. In the second part, we propose a multiobjective formulation for the automatic MOEA design, which proves critical for the context of many-objective optimization due to the disagreement of established performance metrics. Our proposed formulation leads to an automatically designed MOEA that presents state-of-the-art performance according to a set of metrics, rather than a single one.
The optimal placement of electronic components on a printed circuit board is a well-studied optimization task. However, despite the involvement of multiple conflicting objectives, researchers have mainly used a single...
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The optimal placement of electronic components on a printed circuit board is a well-studied optimization task. However, despite the involvement of multiple conflicting objectives, researchers have mainly used a single objective of minimizing the overall wire length or minimizing the overall heat generation or minimizing the overall time delay in its functioning. In this paper, the problem is treated as a two-objective optimization problem of minimizing the overall wire length and minimizing the failure-rate of the board arising due, to uneven local heat accumulation. The proposed strategy uses a novel representation procedure and a multiobjective evolutionary algorithm capable of finding multiple Pareto-optimal solutions simultaneously. Moreover, the flexibility and efficacy of the proposed strategy have been demonstrated by simultaneously optimizing the placement of components and the layout of the board. The convergence and the extent of spread obtained in the solutions reliably by repetitive applications of the proposed procedure should encourage further application of the approach to more complex placement design problems.
This paper analyzes the fault-tolerance nature of evolutionary algorithms (EAs) when executed in a distributed environment subjected to malicious acts. More precisely, the inherent resilience of EAs against two types ...
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This paper analyzes the fault-tolerance nature of evolutionary algorithms (EAs) when executed in a distributed environment subjected to malicious acts. More precisely, the inherent resilience of EAs against two types of failures is considered: (1) crash faults, typically due to resource volatility which lead to data loss and part of the computation loss;(2) cheating faults, a far more complex kind of fault that can be modeled as the alteration of output values produced by some or all tasks of the program being executed. This last type of failure is due to the presence of cheaters on the computing platform. Most often in Global Computing (GC) systems such as BOINC, cheaters are attracted by the various incentives provided to stimulate the volunteers to share their computing resources: cheaters typically seek to obtain rewards with little or no contribution to the system. In this paper, the Algorithm-Based Fault Tolerance (ABFT) aspects of EAs against the above types of faults is characterized. Whereas the inherent resilience of EAs has been previously observed in the literature, for the first time, a formal analysis of the impact of the considered faults over the executed EA including a proof of convergence is proposed in this article. By the variety of problems addressed by EAs, this study will hopefully promote their usage in the future developments around distributed computing platform such as Desktop Grids and Volunteer Computing Systems or Cloud systems where the resources cannot be fully trusted. (C) 2012 Elsevier Ltd. All rights reserved.
Energy efficiency aspects in cellular networks can contribute significantly to reducing worldwide greenhouse gas emissions. The base station (BS) sleeping strategy has become a well-known technique to achieve energy s...
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Energy efficiency aspects in cellular networks can contribute significantly to reducing worldwide greenhouse gas emissions. The base station (BS) sleeping strategy has become a well-known technique to achieve energy savings by switching off redundant BSs mainly for lightly loaded networks. Moreover, introducing renewable energy as an alternative power source has become a real challenge among network operators. In this paper, we formulate an optimization problem that aims to maximize the profit of Long-Term Evolution (LTE) cellular operators and to simultaneously minimize the CO 2 emissions in green wireless cellular networks without affecting the desired quality of service (QoS). The BS sleeping strategy lends itself to an interesting implementation using several heuristic approaches, such as the genetic (GA) and particle swarm optimization (PSO) algorithms. In this paper, we propose GA-based and PSO-based methods that reduce the energy consumption of BSs by not only shutting down underutilized BSs but by optimizing the amounts of energy procured from different retailers (renewable energy and electricity retailers), as well. A comparison with another previously proposed algorithm is also carried out to evaluate the performance and the computational complexity of the employed methods.
Presently, blast furnace-basic oxygen furnace (BF-BOF) route is conventionally used in integrated steel plants (ISPs). As availability and cost of coking coal are becoming serious issues, alternative routes of iron an...
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Presently, blast furnace-basic oxygen furnace (BF-BOF) route is conventionally used in integrated steel plants (ISPs). As availability and cost of coking coal are becoming serious issues, alternative routes of iron and steel production such as COREX, shaft-based process for direct reduction of iron and electric arc furnace in combination with BF-BOF route is becoming a matter of focus. The possibility of energy efficiency improvement using such a mixed route in ISPs is dependent on proper utilization of fuel gases generated in addition to the total input fuel bearing resources to the plant. A flow-sheet simulation approach has been developed using phenomenological and stoichiometric modeling of the important process steps. Optimization for lowering input fuel energy and maximizing available fuel gas energy has been developed based on evolutionary algorithms using simulated flow-sheet streams. The fuel gas used downstream needs to be tailored to higher calorific values (CVs) in the gas network for critical downstream applications. Two cases of high CV-mixed fuel gas have been considered comprising (a) principally mix of coke oven gas and COREX off-gas and (b) mix of blast furnace gas and coke oven gas in 2:1 volume ratio along with high CV gases such as COREX off-gas and BOF converter gas. Energy optimization possibilities exist which simultaneously minimize input fuel energy and maximize high CV fuel gas for downstream use. The flow-sheet solutions simultaneously lead to configuration predictions with fractional stream splits.
A successful implementation of a simulation-optimization (SO) methodology is presented. Based on evolutionary algorithms with a multicriteria fitness function, the new SO is used to developed weekly schedules at a shi...
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A successful implementation of a simulation-optimization (SO) methodology is presented. Based on evolutionary algorithms with a multicriteria fitness function, the new SO is used to developed weekly schedules at a ship building factory that manufactures around 60 jobs per week. Simulation modeling is used to account for randomness on the input data, as well as to correctly abstract the complex operations carried out in the real system. A variant of genetic algorithms is used to search for the appropriate schedule. Its fitness function is a multicriteria process capability index that aggregates three individual criteria, namely, makespan, line blockage and idleness of resources. The index is based on the satisfaction of thresholds for each and every criterion, thresholds that are tightened as improved schedules are found. The thresholds are also used to reject non-promising alternatives without having to perform the same number of runs as for the candidates that stand out for implementation. The name of the methodology is meSO: multicriteria evolutionary SO.
Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maxi...
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Wind farm layout optimisation is a challenging real-world problem which requires the discovery of trade-off solutions considering a variety of conflicting criteria, such as minimisation of the land area usage and maximisation of energy production. However, due to the complexity of handling multiple objectives simultaneously, many approaches proposed in the literature often focus on the optimisation of a single objective when deciding the locations for a set of wind turbines spread across a given region. In this study, we tackle a multi-objective wind farm layout optimisation problem. Different from the previously proposed approaches, we are applying a high-level search method, known as selection hyper heuristic to solve this problem. Selection hyper-heuristics mix and control a predefined set of lowlevel (meta)heuristics which operate on solutions. We test nine different selection hyper-heuristics including an online learning hyper-heuristic on a multi-objective wind farm layout optimisation problem. Our hyper-heuristic approaches manage three well-known multi-objective evolutionary algorithms as low-level metaheuristics. The empirical results indicate the success and potential of selection hyper heuristics for solving this computationally difficult problem. We additionally explore other objectives in wind farm layout optimisation problems to gain a better understanding of the conflicting nature of those objectives. (C) 2016 Elsevier Ltd. All rights reserved.
The constrained minimum-mass problem of middle-size frames is taken into account, for both continuous and discrete cases with ideal (without buckling effect and own gravitational load) and real (with both) models, com...
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The constrained minimum-mass problem of middle-size frames is taken into account, for both continuous and discrete cases with ideal (without buckling effect and own gravitational load) and real (with both) models, comparing three strategies of evolutionary algorithms. Some proposals to obtain appropriate results in middle-sized frames are exposed: optimization considerations about coding and structure;and the introduction of the auto-adaptive rebirth operator. Moreover, the introduction in the initial population of high quality single-optimization solutions obtained via the auto-adaptive rebirth operator is proposed as a way to improve the final non-dominated fronts obtained in structural frame multicriteria optimization (number of different cross-section types as second criteria). The results through three test cases (55-35 bar-sized) show the advantageous use of the auto-adaptive rebirth operator in frame optimization. (C) 2004 Elsevier B.V. All rights reserved.
In orientation to new developments in evolutionary biology we propose an extension of evolutionary algorithms in two dimensions, the regulatory algorithm (RGA). It consists of two levels of vectors, the regulatory vec...
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In orientation to new developments in evolutionary biology we propose an extension of evolutionary algorithms in two dimensions, the regulatory algorithm (RGA). It consists of two levels of vectors, the regulatory vector and the structural vector. Each element of the regulatory vector is connected with one or several elements of the structural vector, but not vice versa. The connections can be interpreted as steering connections, the switching on or off of the structural elements and/or as switching orders for the structural elements. An RGA that operates with the usual genetic operators of mutation and crossover can be used for avoiding rules like penalty or default operators, it is in certain problems significantly faster than a standard genetic algorithm, and it is very suited when modeling and optimizing systems that consist themselves of different levels. Examples of RGA usage are shown, namely, the optimal dividing of socially deviant youths in a hostel, the optimal introduction of communication standards in information systems, and the allocation of employees to superiors by taking into regard the different personality types.
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