Over the last decade, several bio-inspired algorithms have emerged for solving complex optimisation problems. Since the performance of these algorithms present a suboptimal behaviour, a tremendous amount of research h...
详细信息
Over the last decade, several bio-inspired algorithms have emerged for solving complex optimisation problems. Since the performance of these algorithms present a suboptimal behaviour, a tremendous amount of research has been devoted to find new and better optimisation methods. On the other hand, allostasis is a medical term recently coined which explains how the configuration of the internal state (IS) in different organs allows reaching stability when an unbalance condition is presented. In this paper, a novel biologically-inspired algorithm called allostatic optimisation (AO) is proposed for solving optimisation problems. In AO, individuals emulate the IS of different organs. In the approach, each individual is improved by using numerical operators based on the biological principles of the allostasis mechanism. The proposed method has been compared to other well-known optimisation algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.
This paper presents an evolutionary algorithm for the fixed-charge multicommodity network design problem (MCNDP), which concerns routing multiple commodities from origins to destinations by designing a network through...
详细信息
This paper presents an evolutionary algorithm for the fixed-charge multicommodity network design problem (MCNDP), which concerns routing multiple commodities from origins to destinations by designing a network through selecting arcs, with an objective of minimizing the fixed costs of the selected arcs plus the variable costs of the flows on each arc. The proposed algorithm evolves a pool of solutions using principles of scatter search, interlinked with an iterated local search as an improvement method. New cycle-based neighborhood operators are presented which enable complete or partial re-routing of multiple commodities. An efficient perturbation strategy, inspired by ejection chains, is introduced to perform local compound cycle-based moves to explore different parts of the solution space. The algorithm also allows infeasible solutions violating arc capacities while performing the "ejection cycles", and subsequently restores feasibility by systematically applying correction moves. Computational experiments on benchmark MCNDP instances show that the proposed solution method consistently produces high-quality solutions in reasonable computational times. (C) 2016 Published by Elsevier B.V.
In this paper a novel problem in MGs (Migrogrids) is analyzed. The problem consists in the joint optimization of the MG structure and operation, by obtaining on the one hand an optimal sizing of its elements and struc...
详细信息
In this paper a novel problem in MGs (Migrogrids) is analyzed. The problem consists in the joint optimization of the MG structure and operation, by obtaining on the one hand an optimal sizing of its elements and structural parameters, and on the other hand the scheduling for the ESS (energy storage system) in the MG. For this joint problem, a novel two-steps EA (evolutionary algorithm) is proposed. The EA operates in such a way that a first EA obtains the best MG structure, mainly the optimal values for the sizing of generators and ESS, and a second EA determines the operational part of the MG (ESS scheduling). A real scenario of variable electricity prices is considered. In this scenario, power demanded from the main grid has different prices depending on the day and the hour of the day when it is demanded. The MG considered in this paper is formed by wind and photovoltaic generators, different residential and industrial loads, as well as ESS. Moreover, four different settings with different natural resource avail abilities have been analyzed, and the results obtained show a significant cost improvement in the MG's performance. (C) 2015 Elsevier Ltd. All rights reserved.
Since it is difficult for traditional quantum evolutionary algorithm (QEA) to find the global optimal solutions in multimodal optimization problems, an effective co-evolutionary quantum algorithm (ECQA) is proposed in...
详细信息
Since it is difficult for traditional quantum evolutionary algorithm (QEA) to find the global optimal solutions in multimodal optimization problems, an effective co-evolutionary quantum algorithm (ECQA) is proposed in this paper. This ECQA combines the collaborative evolution and adaptive mutation strategy together. The collaborative evolution mechanism is used to make the evolutionary population be divided into multi sub-populations which can complete the evolution process independently and exchange the evolution information effectively. Meanwhile, the adaptive mutation strategy is implemented in order to give the ECQA the power to explore its search space on the basis of full evolutionary information exchange. The experimental simulation results have demonstrated that the proposed algorithm can converge to escape from local extremum points and have better global search capability than QEA, which thus proved the ECQA is effective and feasible for the multimodal functions.
We propose an interactive multiobjective evolutionary algorithm that attempts to discover the most preferred part of the Pareto-optimal set. Preference information is elicited by asking the user to compare some soluti...
详细信息
We propose an interactive multiobjective evolutionary algorithm that attempts to discover the most preferred part of the Pareto-optimal set. Preference information is elicited by asking the user to compare some solutions pairwise. This information is then used to curb the set of compatible user's value functions, and the multiobjective evolutionary algorithm is run to simultaneously search for all solutions that could potentially be the most preferred. Compared to previous similar approaches, we implement a much more efficient way of determining potentially preferred solutions, that is, solutions that are best for at least one value function compatible with the preference information provided by the decision maker. For the first time in the context of evolutionary computation, we apply the Choquet integral as a user's preference model, allowing us to capture interactions between objectives. As there is a trade-off between the flexibility of the value function model and the complexity of learning a faithful model of user's preferences, we propose to start the interactive process with a simple linear model but then to switch to the Choquet integral as soon as the preference information can no longer be represented using the linear model. An experimental analysis demonstrates the effectiveness of the approach. (C) 2015 Elsevier B.V. and Association of European Operational Research Societies (EURO) within the International Federation of Operational Research Societies (IFORS). All rights reserved.
In this participation we discuss the possibility of mutual fusion of evolutionary algorithms and deterministic chaos. As demonstrated in previous research papers, evolutionary algorithms are capable of chaotic system ...
详细信息
In this participation we discuss the possibility of mutual fusion of evolutionary algorithms and deterministic chaos. As demonstrated in previous research papers, evolutionary algorithms are capable of chaotic system control, identification or synthesis and vice versa, chaos can be observed in the evolutionary dynamics. More exactly, in this paper there is numerically demonstrated possible solution of the question whether identification of so-called basin of attraction for hidden attractor can be done by evolutionary algorithms. Hidden attractors are a special kind of attractors, that are hidden in the system structure and if ignored (undiscovered), then can cause serious damages, as already observed in the real world. The research presented here is bivalent. At first it shows, that evolutionary algorithms are able to identify presence of hidden attractors in the system, but also it can be extended to study an existence of hidden attractors in the evolutionary algorithms dynamics. All numerical simulations are demonstrated on Chua's chaotic attractor that contains an example of hidden attractor and at the end there are discussed discrete systems (synthesized by evolution) that likely exhibit hidden attractors, too. (C) 2015 Elsevier Ltd. All rights reserved.
The present work introduces a procedure for input selection and parameter estimation for system identification based on Radial Basis Functions Neural Networks (RBFNNs) models with an improved objective function based ...
详细信息
The present work introduces a procedure for input selection and parameter estimation for system identification based on Radial Basis Functions Neural Networks (RBFNNs) models with an improved objective function based on the residuals and its correlation function coefficients. We show the results when the proposed methodology is applied to model a magnetorheological damper, with real acquired data, and other two well-known benchmarks. The canonical genetic and differential evolution algorithms are used in cascade to decompose the problem of defining the lags taken as the inputs of the model and its related parameters based on the simultaneous minimization of the residuals and higher orders correlation functions. The inner layer of the cascaded approach is composed of a population which represents the lags on the inputs and outputs of the system and an outer layer represents the corresponding parameters of the RBFNN. The approach is able to define both the inputs of the model and its parameters. This is interesting as it frees the designer of manual procedures, which are time consuming and prone to error, usually done to define the model inputs. We compare the proposed methodology with other works found in the literature, showing overall better results for the cascaded approach. (C) 2015 Elsevier Ltd. All rights reserved.
This paper proposes a multi-objective evolutionary algorithm (MOEA)-based task scheduling approach for determining Pareto optimal solutions with simultaneous optimization of performance (P), energy (E), and temperatur...
详细信息
This paper proposes a multi-objective evolutionary algorithm (MOEA)-based task scheduling approach for determining Pareto optimal solutions with simultaneous optimization of performance (P), energy (E), and temperature (T). Our algorithm includes problem-specific solution encoding, determining the initial population of the solution space, and the genetic operators that collectively work on generating efficient solutions in fast turnaround time. Multiple schedules offer a diverse range of values for makespan, energy consumed, and peak temperature and thus present an efficient way of identifying trade-offs among the desired objectives, for a given application and machine pair. We also present a methodology for selecting one solution from the Pareto front given the user's preference. The proposed algorithm for scheduling tasks to cores achieves three-way optimization with fast turnaround time. The proposed algorithm is advantageous because it reduces both energy and temperature together rather than in isolation. We evaluate the proposed algorithm using implementation and simulation, and compare it with integer linear programming as well as with other scheduling algorithms that are energy-or thermal-aware. The time complexity of the proposed scheme is considerably better than the compared algorithms.
作者：
Dino, Ipek GurselUniv Mah
Middle East Tech Univ Dept Architecture Dumlupinar Bulvari 1 TR-06800 Ankara Turkey
This work introduces evolutionary Architectural Space layout Explorer (EASE), a design tool that facilitates the optimization of 3D space layouts. EASE addresses architectural design exploration and the need to attend...
详细信息
This work introduces evolutionary Architectural Space layout Explorer (EASE), a design tool that facilitates the optimization of 3D space layouts. EASE addresses architectural design exploration and the need to attend to many alternatives simultaneously in layout design. For this, we use evolutionary optimization to find a balance between divergent exploration and convergent exploitation. EASE comprises a novel sub-heuristic that constructs valid spatial layouts, a mathematical framework to quantify the satisfaction of constraints, and evolutionary operators to improve alternative layouts' fitness. We test EASE on the design of a library building. We evaluate EASE's performance for different building forms and different evolutionary algorithm parameters. The results suggest that EASE can generate valid layouts, quantify the constraints' degree of satisfaction and find a number of optimal layout solutions. The layouts that EASE generates are intended not as end results but design artifacts that provide insight into the solution space for further exploration. (C) 2016 Elsevier B.V. All rights reserved.
evolutionary algorithms have proved to be efficient approaches in pursuing optimum solutions of multiobjective optimization problems with the number of objectives equal to or less than three. However, the searching pe...
详细信息
evolutionary algorithms have proved to be efficient approaches in pursuing optimum solutions of multiobjective optimization problems with the number of objectives equal to or less than three. However, the searching performance degenerates in high-dimensional objective optimizations. In this paper we propose an algorithm for many-objective optimization with particle swarm optimization as the underlying metaheuristic technique. In the proposed algorithm, the objectives are decomposed and reconstructed using discrete decoupling strategy, and the subgroup procedures are integrated into unified coevolution strategy. The proposed algorithm consists of inner and outer evolutionary processes, together with adaptive factor mu, to maintain convergence and diversity. At the same time, a designed repository reconstruction strategy and improved leader selection techniques of MOPSO are introduced. The comparative experimental results prove that the proposed UMOPSO-D outperforms the other six algorithms, including four common used algorithms and two newly proposed algorithms based on decomposition, especially in high-dimensional targets.
暂无评论