scholarly journals Three Steps to Improve Jellyfish Search Optimiser

MENDEL ◽  
2021 ◽  
Vol 27 (1) ◽  
pp. 29-40
Author(s):  
Petr Bujok
Keyword(s):  
Distribution Coefficient ◽  
Real World ◽  
Population Diversity ◽  
Search Process ◽  
Local Optima ◽  
Statistical Comparison ◽  
Reproduction Process ◽  
The Individual ◽  
Optimisation Methods ◽  
Real World Problems

This paper describes three different mechanisms used in Jellyfish Search (JS) optimiser. At first, an archive of good old solutions is used to prevent getting stuck in the local-optima area. Further, a distribution coefficient beta is adapted during the search process to control population diversity. Finally, an Eigen transformation of individuals in the reproduction process is used occasionally to cope with rotated functions. Three proposed variants of the JS optimiser are compared with the original JS algorithm and nine various well-known Nature-inspired optimisation methods when solving real-world problems of CEC 2011. Provided results achieved by statistical comparison show efficiency of the individual newly employed mechanisms.

scholarly journals A Mutual-Evaluation Genetic Algorithm for Numerical and Routing Optimization

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Chih-Hao Lin ◽  
Jiun-De He
Keyword(s):  
Genetic Algorithm ◽  
Real World ◽  
Function Analysis ◽  
Wide Spectrum ◽  
Solution Space ◽  
Objective Functions ◽  
Routing Problems ◽  
Local Optima ◽  
Mutual Evaluation ◽  
Real World Problems

Many real-world problems can be formulated as numerical optimization with certain objective functions. However, these objective functions often contain numerous local optima, which could trap an algorithm from moving toward the desired global solution. To improve the search efficiency of traditional genetic algorithms, this paper presents a mutual-evaluation genetic algorithm (MEGA). A novel mutual-evaluation approach is employed so that the merit of selected genes in a chromosome can be determined by comparing the fitness changes before and after interchanging with those in the mating chromosome. According to the determined genome merit, a therapy crossover can generate effective schemata to explore the solution space efficiently. The computational experiments for twelve numerical problems show that the MEGA can find near optimal solutions in all test benchmarks and achieve solutions with higher accuracy than those obtained by eight existing algorithms. This study also uses the MEGA to find optimal flow-allocation strategies for multipath-routing problems. Experiments on quality-of-service routing scenarios show that the MEGA can deal with these constrained routing problems effectively and efficiently. Therefore, the MEGA not only can reduce the effort of function analysis but also can deal with a wide spectrum of real-world problems.

Gray Box Optimization for Mk Landscapes (NK Landscapes and MAX-kSAT)

2016 ◽  
Vol 24 (3) ◽  
pp. 491-519 ◽  
Author(s):  
L. Darrell Whitley ◽  
Francisco Chicano ◽  
Brian W. Goldman
Keyword(s):  
Dynamic Programming ◽  
Real World ◽  
Optimization Problems ◽  
Test Problems ◽  
Brute Force ◽  
Local Optima ◽  
Separable Problems ◽  
Boolean Optimization ◽  
Nk Landscapes ◽  
Real World Problems

This article investigates Gray Box Optimization for pseudo-Boolean optimization problems composed of M subfunctions, where each subfunction accepts at most k variables. We will refer to these as Mk Landscapes. In Gray Box Optimization, the optimizer is given access to the set of M subfunctions. We prove Gray Box Optimization can efficiently compute hyperplane averages to solve non-deceptive problems in [Formula: see text] time. Bounded separable problems are also solved in [Formula: see text] time. As a result, Gray Box Optimization is able to solve many commonly used problems from the evolutional computation literature in [Formula: see text] evaluations. We also introduce a more general class of Mk Landscapes that can be solved using dynamic programming and discuss properties of these functions. For certain type of problems Gray Box Optimization makes it possible to enumerate all local optima faster than brute force methods. We also provide evidence that randomly generated test problems are far less structured than those found in real-world problems.

Interdisciplinary Cases and Disciplinary Knowledge

2017 ◽  
Author(s):  
Wolfgang Krohn
Keyword(s):  
Real World ◽  
Causal Explanation ◽  
Individual Case ◽  
Disciplinary Knowledge ◽  
Context Sensitive ◽  
Epistemological Challenge ◽  
Interdisciplinary Projects ◽  
Applicable Knowledge ◽  
The Individual ◽  
Real World Problems

“Interdisciplinary Cases and Disciplinary Knowledge: Epistemic Challenges of Interdisciplinary Research” provides a conceptual framework of interdisciplinarity in the context of contemporary philosophy of science and social epistemology. It describes a widespread tension between the interdisciplinary commitment to complex real-world problems and the disciplinary strategies to build simplified models. While real-world problems call for highly specific and context-sensitive solutions, disciplinary problems serve as exemplars of more a general type. The epistemological challenge of interdisciplinarity is to relate knowledge about complex and singular cases with knowledge about generalized concepts and causalities. This relationship calls for a combination between the “humanistic” ideal of understanding the individual case, and the “scientific” search for common features of different cases. In practice interdisciplinary projects find ways to bridge causal explanation and the concern for the case. An epistemological attempt is made to conceptually integrate the search for universally applicable knowledge and idiographic richness.

scholarly journals Handrails through the Swamp? A Pilot to Test the Integration and Implementation Science Framework in Complex Real-World Research

2021 ◽  
Vol 13 (10) ◽  
pp. 5491
Author(s):  
Melissa Robson-Williams ◽  
Bruce Small ◽  
Roger Robson-Williams ◽  
Nick Kirk
Keyword(s):  
Pilot Study ◽  
Case Studies ◽  
Implementation Science ◽  
Real World ◽  
Environmental Problems ◽  
Environmental Research ◽  
Integrative Framework ◽  
The World ◽  
Science Framework ◽  
Real World Problems

The socio-environmental challenges the world faces are ‘swamps’: situations that are messy, complex, and uncertain. The aim of this paper is to help disciplinary scientists navigate these swamps. To achieve this, the paper evaluates an integrative framework designed for researching complex real-world problems, the Integration and Implementation Science (i2S) framework. As a pilot study, we examine seven inter and transdisciplinary agri-environmental case studies against the concepts presented in the i2S framework, and we hypothesise that considering concepts in the i2S framework during the planning and delivery of agri-environmental research will increase the usefulness of the research for next users. We found that for the types of complex, real-world research done in the case studies, increasing attention to the i2S dimensions correlated with increased usefulness for the end users. We conclude that using the i2S framework could provide handrails for researchers, to help them navigate the swamps when engaging with the complexity of socio-environmental problems.

scholarly journals Combined Games with Randomly Delayed Beginnings

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 534
Author(s):  
F. Thomas Bruss
Keyword(s):  
Discrete Time ◽  
Real World ◽  
Optimal Stopping ◽  
Random Variables ◽  
Approximate Solutions ◽  
Real World Problems

This paper presents two-person games involving optimal stopping. As far as we are aware, the type of problems we study are new. We confine our interest to such games in discrete time. Two players are to chose, with randomised choice-priority, between two games G1 and G2. Each game consists of two parts with well-defined targets. Each part consists of a sequence of random variables which determines when the decisive part of the game will begin. In each game, the horizon is bounded, and if the two parts are not finished within the horizon, the game is lost by definition. Otherwise the decisive part begins, on which each player is entitled to apply their or her strategy to reach the second target. If only one player achieves the two targets, this player is the winner. If both win or both lose, the outcome is seen as “deuce”. We motivate the interest of such problems in the context of real-world problems. A few representative problems are solved in detail. The main objective of this article is to serve as a preliminary manual to guide through possible approaches and to discuss under which circumstances we can obtain solutions, or approximate solutions.

Review of Network Flow Algorithms David P. Williamson

2021 ◽  
Vol 52 (1) ◽  
pp. 12-15
Author(s):  
S.V. Nagaraj
Keyword(s):  
Graduate Students ◽  
Real World ◽  
Network Flow ◽  
Network Flows ◽  
Optimization Problems ◽  
Capacity Constraints ◽  
Incoming Flow ◽  
Network Flow Problems ◽  
Flow Problems ◽  
Real World Problems

This book is on algorithms for network flows. Network flow problems are optimization problems where given a flow network, the aim is to construct a flow that respects the capacity constraints of the edges of the network, so that incoming flow equals the outgoing flow for all vertices of the network except designated vertices known as the source and the sink. Network flow algorithms solve many real-world problems. This book is intended to serve graduate students and as a reference. The book is also available in eBook (ISBN 9781316952894/US$ 32.00), and hardback (ISBN 9781107185890/US$99.99) formats. The book has a companion web site www.networkflowalgs.com where a pre-publication version of the book can be downloaded gratis.

Help communities solve real-world problems with AI

AI Matters ◽  
2019 ◽  
Vol 5 (3) ◽  
pp. 12-14
Author(s):  
Tara Chklovski
Keyword(s):  
Real World ◽  
Real World Problems
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