We develop a population-based algorithm for the optimization of multiple, nonconvex, nondifferentiable, and possibly discontinuous objective functions. The algorithm employs Markov kernels, Hit-and-Run, and Pattern Hi...
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We develop a population-based algorithm for the optimization of multiple, nonconvex, nondifferentiable, and possibly discontinuous objective functions. The algorithm employs Markov kernels, Hit-and-Run, and Pattern Hit-and-Run for exploration of the solution space and Pareto ordering rules for the selection of the population and to update the approximate Pareto optimal list. Our multiobjective interacting particle algorithm asymptotically converges to the stationary distribution associated with the Pareto ordering rules. We present numerical benchmark results on test problems.
In this study, a novel meta-heuristic search (MHS) algorithm for constrained global optimization problems is proposed. Since many algorithms aim to achieve well-balanced exploitation-exploration stages with often unsa...
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In this study, a novel meta-heuristic search (MHS) algorithm for constrained global optimization problems is proposed. Since many algorithms aim to achieve well-balanced exploitation-exploration stages with often unsatisfactory results, in the approach introduced in this paper, Attraction-Repulsion Optimization Algorithm (AROA), the balance associated with attraction-repulsion phenomena that occur in nature is mimicked. AROA introduces a search strategy in which a candidate solution is moved in the search space depending on the quality of solutions in its neighborhood, as well as the best candidate. The candidates are managed by local search operators based on modified Brownian motion, trigonometric functions, randomly selected solutions, and a form of memory. Consequently, AROA exhibits a satisfactory exploitation-exploration balance exhibited by highly competitive performance. The introduced algorithm is experimentally compared with the state-ofthe-art meta-heuristics on the CEC 2014, 2017, and 2020 test suites. The obtained results reveal the advantages of AROA over related algorithms and its suitability in solving complex real-world problems.
Memorizing the past information for later environments is an effective and widely employed approach to optimize dynamic problems. Although the existing explicit memories for dynamic optimization differ widely in the l...
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Memorizing the past information for later environments is an effective and widely employed approach to optimize dynamic problems. Although the existing explicit memories for dynamic optimization differ widely in the literature, all of them organize memory entries in a linear list. This naive structure leads to problems, such as heavy computational overhead and small memory capacity, and thus restricts the performance of the memories. In this paper, the binary space partition tree is adopted to organize the memory entries, and then a memory tree is constructed. The memory tree partitions the search space into regions. In order to make use of the memory tree, a neighbor shift strategy is proposed. When a new individual is generated in a region that has never been visited since the last change, the new individual is shifted to the neighboring memory individual of that region, if it is less fit than the memory individual. The proposed approach can be easily combined with many population-based algorithms for dynamic optimization in the real space. As examples, the proposed approach was combined with a basic particle swarm optimizer and two state-of-the-art dynamic optimizers. The experimental results showed that it significantly enhanced the performance of the three optimizers on various test problems. The proposed approach demonstrates the importance of memory structure in memory approaches.
In this paper, we present a multi-objective simulation-based headway optimization for complex urban mass rapid transit systems. Real-world applications often confront conflicting goals of cost versus service level. We...
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In this paper, we present a multi-objective simulation-based headway optimization for complex urban mass rapid transit systems. Real-world applications often confront conflicting goals of cost versus service level. We propose a two-phase algorithm that combines the single-objective covariance matrix adaptation evolution strategy with a problem-specific multi-directional local search. With a computational study, we compare our proposed method against both a multi-objective covariance matrix adaptation evolution strategy and a non-dominated sorting genetic algorithm. The integrated discrete event simulation model has several stochastic elements. Fluctuating demand (i.e., creation of passengers) is driven by hourly origin-destination-matrices based on mobile phone and infrared count data. We also consider the passenger distribution along waiting platforms and within vehicles. Our two-phase optimization scheme outperforms the comparative approaches, in terms of both spread and the accuracy of the resulting Pareto front approximation.
We introduce a dynamical annealing schedule for population-based optimization algorithms with mutation. On the basis of a statistical mechanics formulation of the population dynamics, the mutation rate adapts to a val...
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We introduce a dynamical annealing schedule for population-based optimization algorithms with mutation. On the basis of a statistical mechanics formulation of the population dynamics, the mutation rate adapts to a value maximizing expected rewards at each time step. Thereby, the mutation rate is eliminated as a free parameter from the algorithm.
The paper presents three approximation algorithms for solving the generalized segregated storage problem (GSSP). GSSP involves determining an optimal distribution of goods among a set of storage compartments with the ...
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The paper presents three approximation algorithms for solving the generalized segregated storage problem (GSSP). GSSP involves determining an optimal distribution of goods among a set of storage compartments with the segregation (physical separation) restrictions. GSSP is a new generalization of well-known segregated storage problem. The paper gives problem formulation and proposes three approximation algorithms for solving it: a specialized construction heuristic and two population-based algorithms: an evolutionary algorithm and a population learning algorithm. The algorithms are evaluated in computational experiments. The analysis of variance method was used for statistical analysis of obtained results. (C) 2003 Elsevier B.V. All rights reserved.
The OneMax problem, alternatively known as the Hamming distance problem, is often referred to as the "drosophila of evolutionary computation (EC)", because of its high relevance in theoretical and empirical ...
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ISBN:
(纸本)9783030581145;9783030581152
The OneMax problem, alternatively known as the Hamming distance problem, is often referred to as the "drosophila of evolutionary computation (EC)", because of its high relevance in theoretical and empirical analyses of EC approaches. It is therefore surprising that even for the simplest of all mutation-basedalgorithms, Randomized Local Search and the (1 + 1) EA, the optimal mutation rates were determined only very recently, in a GECCO 2019 poster. In this work, we extend the analysis of optimal mutation rates to two variants of the (1 + lambda) EA and to the (1 + lambda) RLS. To do this, we use dynamic programming and, for the (1 + lambda) EA, numeric optimization, both requiring Theta(n(3)) time for problem dimension n. With this in hand, we compute for all population sizes lambda is an element of {2(i) vertical bar 0 <= i <= 18} and for problem dimension n is an element of {1000, 2000, 5000} which mutation rates minimize the expected running time and which ones maximize the expected progress. Our results do not only provide a lower bound against which we can measure common evolutionary approaches, but we also obtain insight into the structure of these optimal parameter choices. For example, we show that, for large population sizes, the best number of bits to flip is not monotone in the distance to the optimum. We also observe that the expected remaining running times are not necessarily unimodal for the (1 + lambda) EA(0 -> 1) with shifted mutation.
This study proposes a new strategy to improve the performance of the algorithms of the Fish School Search (FSS) family via the individualization of the step-size of each fish. We propose to be calculated in two differ...
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ISBN:
(数字)9781665467087
ISBN:
(纸本)9781665467087
This study proposes a new strategy to improve the performance of the algorithms of the Fish School Search (FSS) family via the individualization of the step-size of each fish. We propose to be calculated in two different manners: using individual weight or using individual fitness, depending on the chosen variation of the proposed technique. Our methods were tested on the original FSS, on the Weight based Fish School Search (wFSS) and on the Multi Objective Fish School Search (MOFSS) algorithms. The benchmark functions of the Congress on Evolutionary Computation, The Genetic and Evolutionary Computation Conference (CEC'2020, CEC'2013, and GECCO'2016) and the DTLZ test suite were used to assess the experimental results, which yielded that all variants of the FSS algorithm tested have been improved in the majority of the scenarios.
In this research work a large set of the classical numerical functions were taken into account in order to understand both the search capability and the ability to escape from a local optimal of a clonal selection alg...
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ISBN:
(纸本)9783642145469
In this research work a large set of the classical numerical functions were taken into account in order to understand both the search capability and the ability to escape from a local optimal of a clonal selection algorithm, called i-CSA. The algorithm was extensively compared against several variants of Differential Evolution (DE) algorithm, and with some typical swarm intelligence algorithms. The obtained results show as i-CSA is effective in terms of accuracy, and it is able to solve large-scale instances of well-known benchmarks. Experimental results also indicate that the algorithm is comparable, and often outperforms, the compared nature-inspired approaches. From the experimental results, it is possible to note that a longer maturation of a B cell, inside the population, assures the achievement of better solutions;the maturation period affects the diversity and the effectiveness of the immune search process on a specific problem instance. To assess the learning capability during the evolution of the algorithm three different relative entropies were used: Kullback-Leibler, Renyi generalized and Von Neumann divergences. The adopted entropic divergences show a strong correlation between optima discovering, and high relative entropy values.
In the last decades, there has been a tendency to move away from mathematically tractable, but simplistic models towards more sophisticated and real-world models in finance. However, the consequence of the improved so...
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ISBN:
(数字)9783790826043
ISBN:
(纸本)9783790826036
In the last decades, there has been a tendency to move away from mathematically tractable, but simplistic models towards more sophisticated and real-world models in finance. However, the consequence of the improved sophistication is that the model specification and analysis is no longer mathematically tractable. Instead solutions need to be numerically approximated. For this task, evolutionary computation heuristics are the appropriate means, because they do not require any rigid mathematical properties of the model. Evolutionary algorithms are search heuristics, usually inspired by Darwinian evolution and Mendelian inheritance, which aim to determine the optimal solution to a given problem by competition and alteration of candidate solutions of a population. In this work, we focus on credit risk modelling and financial portfolio optimization to point out how evolutionary algorithms can easily provide reliable and accurate solutions to challenging financial problems.
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