A Bio-Inspired Method for Mathematical Optimization Inspired by Arachnida Salticidade

This paper proposes a new meta-heuristic called Jumping Spider Optimization Algorithm (JSOA), inspired by Arachnida Salticidae hunting habits. The proposed algorithm mimics the behavior of spiders in nature and mathematically models its hunting strategies: search, persecution, and jumping skills to get the prey. These strategies provide a fine balance between exploitation and exploration over the solution search space and solve global optimization problems. JSOA is tested with 20 well-known testbench mathematical problems taken from the literature. Further studies include the tuning of a Proportional-Integral-Derivative (PID) controller, the Selective harmonic elimination problem, and a few real-world single objective bound-constrained numerical optimization problems taken from CEC 2020. Additionally, the JSOA’s performance is tested against several well-known bio-inspired algorithms taken from the literature. The statistical results show that the proposed algorithm outperforms recent literature algorithms and is capable to solve challenging real-world problems with unknown search space.

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Review of Network Flow Algorithms David P. Williamson

ACM SIGACT News ◽

10.1145/3457588.3457592 ◽

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.

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Opposition-based learning Multi-Verse Optimizer with disruption operator for optimization problems

10.21203/rs.3.rs-277857/v1 ◽

2021 ◽

Author(s):

Mohammad Shehab ◽

Laith Abualigah

Keyword(s):

Real World ◽

Optimization Problems ◽

Second Stage ◽

Opposition Based Learning ◽

High Fitness ◽

Fitness Value ◽

Speed Up ◽

Disruption Operator ◽

The Given ◽

Real World Problems

Abstract Multi-Verse Optimizer (MVO) algorithm is one of the recent metaheuristic algorithms used to solve various problems in different fields. However, MVO suffers from a lack of diversity which may trapping of local minima, and premature convergence. This paper introduces two steps of improving the basic MVO algorithm. The first step using Opposition-based learning (OBL) in MVO, called OMVO. The OBL aids to speed up the searching and improving the learning technique for selecting a better generation of candidate solutions of basic MVO. The second stage, called OMVOD, combines the disturbance operator (DO) and OMVO to improve the consistency of the chosen solution by providing a chance to solve the given problem with a high fitness value and increase diversity. To test the performance of the proposed models, fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems, and seven CEC 2011 real-world problems were used in both phases of the enhancement. The second step, known as OMVOD, incorporates the disruption operator (DO) and OMVO to improve the accuracy of the chosen solution by giving a chance to solve the given problem with a high fitness value while also increasing variety. Fifteen CEC 2015 benchmark functions problems, thirty CEC 2017 benchmark functions problems and seven CEC 2011 real-world problems were used in both phases of the upgrade to assess the accuracy of the proposed models.

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An Integrated Design Optimization Approach for Quantitative and Qualitative Search Space

Volume 2: 29th Design Automation Conference, Parts A and B ◽

10.1115/detc2003/dac-48852 ◽

2003 ◽

Cited By ~ 2

Author(s):

Victor Oduguwa ◽

Rajkumar Roy ◽

Didier Farrugia

Keyword(s):

Real World ◽

Design Problem ◽

Integrated Design ◽

Search Space ◽

Optimization Approach ◽

Design Optimisation ◽

Solution Approach ◽

Rod Rolling ◽

Algorithmic Engineering ◽

Real World Problems

Most of the algorithmic engineering design optimisation approaches reported in the literature aims to find the best set of solutions within a quantitative (QT) search space of the given problem while ignoring related qualitative (QL) issues. These QL issues can be very important and by ignoring them in the optimisation search, can have expensive consequences especially for real world problems. This paper presents a new integrated design optimisation approach for QT and QL search space. The proposed solution approach is based on design of experiment methods and fuzzy logic principles for building the required QL models, and evolutionary multi-objective optimisation technique for solving the design problem. The proposed technique was applied to a two objectives rod rolling problem. The results obtained demonstrate that the proposed solution approach can be used to solve real world problems taking into account the related QL evaluation of the design problem.

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Bipolar switching waveform: Novel solution sets to the selective harmonic elimination problem

2010 IEEE International Conference on Industrial Technology ◽

10.1109/icit.2010.5472718 ◽

2010 ◽

Cited By ~ 6

Author(s):

Georgios S. Konstantinou ◽

Vassilios G. Agelidis

Keyword(s):

Solution Sets ◽

Bipolar Switching ◽

Harmonic Elimination ◽

Selective Harmonic Elimination ◽

Elimination Problem

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AEGA: A New Real-Coded Genetic AlgorithmTaking Account of Extrapolation

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2016.p0429 ◽

2016 ◽

Vol 20 (3) ◽

pp. 429-437 ◽

Cited By ~ 1

Author(s):

Kento Uemura ◽

◽

Isao Ono

Keyword(s):

Real World ◽

Optimization Problems ◽

Function Optimization ◽

Benchmark Problems ◽

Lens Design ◽

Real World Problem ◽

Real Coded Genetic Algorithm ◽

Stable Manner ◽

Ridge Structures ◽

Real World Problems

This study proposes a new real-coded genetic algorithm (RCGA) taking account of extrapolation, which we call adaptive extrapolation RCGA (AEGA). Real-world problems are often formulated as black-box function optimization problems and sometimes have ridge structures and implicit active constraints. mAREX/JGG is one of the most powerful RCGAs that performs well against these problems. However, mAREX/JGG has a problem of search inefficiency. To overcome this problem, we propose AEGA that generates offspring outside the current population in a more stable manner than mAREX/JGG. Moreover, AEGA adapts the width of the offspring distribution automatically to improve its search efficiency. We evaluate the performance of AEGA using benchmark problems and show that AEGA finds the optimum with fewer evaluations than mAREX/JGG with a maximum reduction ratio of 45%. Furthermore, we apply AEGA to a lens design problem that is known as a difficult real-world problem and show that AEGA reaches the known best solution with approximately 25% fewer evaluations than mAREX/JGG.

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Putting Continuous Metaheuristics to Work in Binary Search Spaces

Complexity ◽

10.1155/2017/8404231 ◽

2017 ◽

Vol 2017 ◽

pp. 1-19 ◽

Cited By ~ 60

Author(s):

Broderick Crawford ◽

Ricardo Soto ◽

Gino Astorga ◽

José García ◽

Carlos Castro ◽

...

Keyword(s):

Real World ◽

Optimization Problems ◽

Continuous Optimization ◽

Search Space ◽

Binary Search ◽

The Real ◽

Search Spaces ◽

Binary Problems

In the real world, there are a number of optimization problems whose search space is restricted to take binary values; however, there are many continuous metaheuristics with good results in continuous search spaces. These algorithms must be adapted to solve binary problems. This paper surveys articles focused on the binarization of metaheuristics designed for continuous optimization.

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Parallel resultant elimination algorithm to solve the selective harmonic elimination problem

IET Power Electronics ◽

10.1049/iet-pel.2015.0070 ◽

2016 ◽

Vol 9 (1) ◽

pp. 71-80 ◽

Cited By ~ 16

Author(s):

Kehu Yang ◽

Liangyu Chen ◽

Jianjun Zhang ◽

Jun Hao ◽

Wensheng Yu

Keyword(s):

Elimination Algorithm ◽

Harmonic Elimination ◽

Selective Harmonic Elimination ◽

Elimination Problem

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A Survey on Meta-heuristic Algorithms for Global Optimization Problems

10.54216/jisiot.010104 ◽

2020 ◽

pp. 48-60

Author(s):

Abdel Nasser H. Zaied ◽

Mahmoud Ismail ◽

Salwa El-Sayed ◽

◽

...

Keyword(s):

Global Optimization ◽

Real World ◽

Heuristic Algorithms ◽

Optimization Problems ◽

Optimization Algorithms ◽

Traditional Methods ◽

Complex Problems ◽

First Choice ◽

Important Field ◽

Real World Problems

Optimization is a more important field of research. With increasing the complexity of real-world problems, the more efficient and reliable optimization algorithms vital. Traditional methods are unable to solve these problems so, the first choice for solving these problems becomes meta-heuristic algorithms. Meta-heuristic algorithms proved their ability to solve more complex problems and giving more satisfying results. In this paper, we introduce the more popular meta-heuristic algorithms and their applications in addition to providing the more recent references for these algorithms.

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Decomposition Algorithms for a Multi-Hard Problem

Evolutionary Computation ◽

10.1162/evco_a_00211 ◽

2018 ◽

Vol 26 (3) ◽

pp. 507-533 ◽

Cited By ~ 1

Author(s):

M. R. Przybylek ◽

A. Wierzbicki ◽

Z. Michalewicz

Keyword(s):

Systematic Study ◽

Real World ◽

Optimization Problems ◽

Decomposition Algorithms ◽

Hard Problem ◽

The Past ◽

Real World Problems

Real-world optimization problems have been studied in the past, but the work resulted in approaches tailored to individual problems that could not be easily generalized. The reason for this limitation was the lack of appropriate models for the systematic study of salient aspects of real-world problems. The aim of this article is to study one of such aspects: multi-hardness. We propose a variety of decomposition-based algorithms for an abstract multi-hard problem and compare them against the most promising heuristics.

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Optimization of Chance-Constrained Submodular Functions

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i02.5504 ◽

2020 ◽

Vol 34 (02) ◽

pp. 1460-1467

Author(s):

Benjamin Doerr ◽

Carola Doerr ◽

Aneta Neumann ◽

Frank Neumann ◽

Andrew Sutton

Keyword(s):

Real World ◽

Optimization Problems ◽

Greedy Algorithms ◽

Chance Constraints ◽

Chance Constraint ◽

High Quality ◽

Submodular Optimization ◽

Network Problems ◽

Surrogate Functions ◽

Real World Problems

Submodular optimization plays a key role in many real-world problems. In many real-world scenarios, it is also necessary to handle uncertainty, and potentially disruptive events that violate constraints in stochastic settings need to be avoided. In this paper, we investigate submodular optimization problems with chance constraints. We provide a first analysis on the approximation behavior of popular greedy algorithms for submodular problems with chance constraints. Our results show that these algorithms are highly effective when using surrogate functions that estimate constraint violations based on Chernoff bounds. Furthermore, we investigate the behavior of the algorithms on popular social network problems and show that high quality solutions can still be obtained even if there are strong restrictions imposed by the chance constraint.

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