10,495 research outputs found
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Three decades of the Shuffled Complex Evolution (SCE-UA) optimization algorithm: Review and applications
Negatively Correlated Search
Evolutionary Algorithms (EAs) have been shown to be powerful tools for
complex optimization problems, which are ubiquitous in both communication and
big data analytics. This paper presents a new EA, namely Negatively Correlated
Search (NCS), which maintains multiple individual search processes in parallel
and models the search behaviors of individual search processes as probability
distributions. NCS explicitly promotes negatively correlated search behaviors
by encouraging differences among the probability distributions (search
behaviors). By this means, individual search processes share information and
cooperate with each other to search diverse regions of a search space, which
makes NCS a promising method for non-convex optimization. The cooperation
scheme of NCS could also be regarded as a novel diversity preservation scheme
that, different from other existing schemes, directly promotes diversity at the
level of search behaviors rather than merely trying to maintain diversity among
candidate solutions. Empirical studies showed that NCS is competitive to
well-established search methods in the sense that NCS achieved the best overall
performance on 20 multimodal (non-convex) continuous optimization problems. The
advantages of NCS over state-of-the-art approaches are also demonstrated with a
case study on the synthesis of unequally spaced linear antenna arrays
Design of evacuation plans for densely urbanised city centres
The high population density and tightly packed nature of some city centres make emergency planning for these urban spaces especially important, given the potential for human loss in case of disaster. Historic and recent events have made emergency service planners particularly conscious of the need for preparing evacuation plans in advance. This paper discusses a methodological approach for assisting decision-makers in designing urban evacuation plans. The approach aims at quickly and safely moving the population away from the danger zone into shelters. The plans include determining the number and location of rescue facilities, as well as the paths that people should take from their building to their assigned shelter in case of an occurrence requiring evacuation. The approach is thus of the location–allocation–routing type, through the existing streets network, and takes into account the trade-offs among different aspects of evacuation actions that inevitably come up during the planning stage. All the steps of the procedure are discussed and systematised, along with computational and practical implementation issues, in the context of a case study – the design of evacuation plans for the historical centre of an old European city
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Finding High-Dimensional D-OptimalDesigns for Logistic Models via Differential Evolution
D-optimal designs are frequently used in controlled experiments to obtain the most accurateestimate of model parameters at minimal cost. Finding them can be a challenging task, especially whenthere are many factors in a nonlinear model. As the number of factors becomes large and interact withone another, there are many more variables to optimize and the D-optimal design problem becomes highdimensionaland non-separable. Consequently, premature convergence issues arise. Candidate solutions gettrapped in local optima and the classical gradient-based optimization approaches to search for the D-optimaldesigns rarely succeed. We propose a specially designed version of differential evolution (DE) which is arepresentative gradient-free optimization approach to solve such high-dimensional optimization problems.The proposed specially designed DE uses a new novelty-based mutation strategy to explore the variousregions in the search space. The exploration of the regions will be carried out differently from the previouslyexplored regions and the diversity of the population can be preserved. The proposed novelty-based mutationstrategy is collaborated with two common DE mutation strategies to balance exploration and exploitationat the early or medium stage of the evolution. Additionally, we adapt the control parameters of DE as theevolution proceeds. Using logistic models with several factors on various design spaces as examples, oursimulation results show our algorithm can find D-optimal designs efficiently and the algorithm outperformsits competitors. As an application, we apply our algorithm and re-design a 10-factor car refueling experimentwith discrete and continuous factors and selected pairwise interactions. Our proposed algorithm was able toconsistently outperform the other algorithms and find a more efficient D-optimal design for the problem
Scientific progress despite irreproducibility: A seeming paradox
It appears paradoxical that science is producing outstanding new results and
theories at a rapid rate at the same time that researchers are identifying
serious problems in the practice of science that cause many reports to be
irreproducible and invalid. Certainly the practice of science needs to be
improved and scientists are now pursuing this goal. However, in this
perspective we argue that this seeming paradox is not new, has always been part
of the way science works, and likely will remain so. We first introduce the
paradox. We then review a wide range of challenges that appear to make
scientific success difficult. Next, we describe the factors that make science
work-in the past, present, and presumably also in the future. We then suggest
that remedies for the present practice of science need to be applied
selectively so as not to slow progress, and illustrate with a few examples. We
conclude with arguments that communication of science needs to emphasize not
just problems but the enormous successes and benefits that science has brought
and is now bringing to all elements of modern society.Comment: 3 figure
New challenges for business actors and positive heuristics
Purpose: The purpose of this guest editorial is to present an overview of the contributions in this special issue and proposes a positive approach to heuristics deriving from the growing interest in the decision-making topic with respect to the new challenges emerging in uncertain environments in management and marketing research. Design/methodology/approach: The authors explore the reasons for a positive view of business actors' judgments and choices based on heuristics, not only in terms of effectiveness in practice, but their fit with human cognition and behavior, and the potential distinctiveness in contexts where technological devices and algorithms are more widespread, but not necessarily more appropriate. Findings: The authors present and discuss the emergence and evolution of heuristics as a topic in the management literature, and the themes and insights proposed in the papers published in this special issue contributing to research aimed at systemizing a managerial perspective of the concepts and tools that may be useful for practitioners and researchers in this field. Originality/value: The paper discusses the positive role that heuristics can play, offering some propositions for future research by framing heuristics as a set of tools (toolbox) for business actors in uncertain contexts, without constituting a cognitive limitation for effective solutions
Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer
We solve a multi-period portfolio optimization problem using D-Wave Systems'
quantum annealer. We derive a formulation of the problem, discuss several
possible integer encoding schemes, and present numerical examples that show
high success rates. The formulation incorporates transaction costs (including
permanent and temporary market impact), and, significantly, the solution does
not require the inversion of a covariance matrix. The discrete multi-period
portfolio optimization problem we solve is significantly harder than the
continuous variable problem. We present insight into how results may be
improved using suitable software enhancements, and why current quantum
annealing technology limits the size of problem that can be successfully solved
today. The formulation presented is specifically designed to be scalable, with
the expectation that as quantum annealing technology improves, larger problems
will be solvable using the same techniques.Comment: 7 pages; expanded and update
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