scholarly journals Tree-Seed Algorithm for Large-Scale Binary Optimization

2018 ◽  
Vol 3 (1) ◽  
pp. 48 ◽  
Author(s):  
Ahmet Cevahir Cinar ◽  
Hazim Iscan ◽  
Mustafa Servet Kiran
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Logic Gate ◽  
Continuous Optimization ◽  
Population Based ◽  
Benchmark Problems ◽  
Binary Optimization ◽  
Continuous Optimization Problems ◽  
Computation Algorithms ◽  
Tree Seed

Population-based swarm or evolutionary computation algorithms in optimization are attracted the interest of the researchers due their simple structure, optimization performance, easy-adaptation. Binary optimization problems can be also solved by using these algorithms. This paper focuses on solving large scale binary optimization problems by using Tree-Seed Algorithm (TSA) proposed for solving continuous optimization problems by imitating relationship between the trees and their seeds in nature. The basic TSA is modified by using xor logic gate for solving binary optimization problems in this study. In order to investigate the performance of the proposed algorithm, the numeric benchmark problems with the different dimensions are considered and obtained results show that the proposed algorithm produces effective and comparable solutions in terms of solution quality.Keywords: binary optimization, tree-seed algorithm, xor-gate, large-scale optimization

Novel Discrete Rao Algorithms for Solving the Travelling Salesman Problem

2021 ◽  
Vol 12 (3) ◽  
pp. 44-61
Author(s):  
Ankit Kumar Nikum
Keyword(s):  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Continuous Optimization ◽  
Bat Algorithm ◽  
Cuckoo Search ◽  
Population Based ◽  
Discrete Form ◽  
Mathematical Equations ◽  
Continuous Optimization Problems

Rao algorithms are population-based metaphor-less optimization algorithms. Rao algorithms consist of three algorithms characterized by three mathematical equations. These algorithms use the characteristics of the best and worst solution to modify the current population along with some characteristics of a random solution. These algorithms are found to be very efficient for continuous optimization problems. In this paper, efforts are made to convert Rao 1 algorithm to its discrete form. This paper proposes three techniques for converting these continuous Rao algorithms to their discrete form. One of the techniques is based on swap operator used for transforming PSO to discrete PSO, and the other two techniques are based on two novel mutating techniques. The algorithms are applied to symmetric TSP problems, and the results are compared with different state of the art algorithms, including discrete bat algorithm (DBA), discrete cuckoo search (DCS), ant colony algorithm, and GA. The results of Rao algorithms are highly competitive compared to the rest of the algorithms

scholarly journals A survey on evolutionary computation for complex continuous optimization

2021 ◽  
Author(s):  
Zhi-Hui Zhan ◽  
Lin Shi ◽  
Kay Chen Tan ◽  
Jun Zhang
Keyword(s):  
Evolutionary Computation ◽  
Large Scale ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Future Research ◽  
Practical Applications ◽  
Fast Development ◽  
Comprehensive Survey ◽  
Continuous Optimization Problems ◽  
Expensive Optimization

AbstractComplex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

Hybrid Genetic Algorithm

2014 ◽  
pp. 268-305 ◽  
Author(s):  
Kedar Nath Das
Keyword(s):  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Continuous Optimization ◽  
Benchmark Problems ◽  
Future Research ◽  
Test Bed ◽  
Unconstrained Optimization Problems ◽  
Power Mutation ◽  
Continuous Optimization Problems ◽  
Real Coded Genetic Algorithms

Real coded Genetic Algorithms (GAs) are the most effective and popular techniques for solving continuous optimization problems. In the recent past, researchers used the Laplace Crossover (LX) and Power Mutation (PM) in the GA cycle (namely LX-PM) efficiently for solving both constrained and unconstrained optimization problems. In this chapter, a local search technique, namely Quadratic Approximation (QA) is discussed. QA is hybridized with LX-PM in order to improve its efficiency and efficacy. The generated hybrid system is named H-LX-PM. The supremacy of H-LX-PM over LX-PM is validated through a test bed of 22 unconstrained and 15 constrained typical benchmark problems. In the later part of this chapter, a few applications of GA in networking optimization are highlighted as the scope for future research.

Spiral Dynamics Inspired Optimization

2011 ◽  
Vol 15 (8) ◽  
pp. 1116-1122 ◽  
Author(s):  
Kenichi Tamura ◽  
◽  
Keiichiro Yasuda
Keyword(s):  
Optimization Problems ◽  
Dimensional Space ◽  
Continuous Optimization ◽  
Search Method ◽  
Benchmark Problems ◽  
Rotation Matrices ◽  
Spiral Dynamics ◽  
Logarithmic Spirals ◽  
Continuous Optimization Problems ◽  
Spiral Model

We recently proposed a new multipoint search method for 2-dimensional continuous optimization problems based on an analogy of spiral phenomena called 2-dimensional spiral optimization. Focused spiral phenomena, which appear frequently in nature, are approximated to logarithmic spirals. Two-dimensional spiral optimization used a feature of logarithmic spirals. In this paper, we proposen-dimensional spiral optimization by extending the 2-dimensional one. The n-dimensional spiral model is constructed based on rotation matrices defined inn-dimensional space. Simulation results for different benchmark problems show the effectiveness of our proposal compared to other metaheuristics.

An Algorithm for Continuous Optimization Problems using Hybrid Particle Updating Method

2016 ◽  
Vol 3 (1) ◽  
pp. 164
Author(s):  
Peter Bamidele Shola ◽  
L B Asaju
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Population Based ◽  
Global Optimum ◽  
Swarm Optimization ◽  
Standard Particle Swarm Optimization ◽  
Continuous Optimization Problems ◽  
Number Of Iterations

<p>Optimization problem is one such problem commonly encountered in many area of endeavor, obviously due to the need to economize the use of the available resources in many problems. This paper presents a population-based meta-heuristic algorithm   for solving optimization problems in a continous space. The algorithm, combines a form of cross-over technique with a position updating formula based on the instantaneous global best position to update each particle position .The algorithm was tested and compared with the standard particle swarm optimization (PSO)  on many benchmark functions. The result suggests a better performance of the algorithm over the later in terms of reaching (attaining) the global optimum value (at least for those benchmark functions considered) and the rate of convergence in terms of the number of iterations required reaching the optimum values.</p>

TSASC: tree–seed algorithm with sine–cosine enhancement for continuous optimization problems

2020 ◽  
Vol 24 (24) ◽  
pp. 18627-18646
Author(s):  
Jianhua Jiang ◽  
Rui Han ◽  
Xianqiu Meng ◽  
Keqin Li
Keyword(s):  
Optimization Problems ◽  
Continuous Optimization ◽  
Continuous Optimization Problems ◽  
Tree Seed

scholarly journals A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems

Mathematics ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 1056 ◽  
Author(s):  
Feng ◽  
Yu ◽  
Wang
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Orthogonal Design ◽  
Continuous Optimization ◽  
Monarch Butterfly ◽  
Migration Behavior ◽  
Monarch Butterflies ◽  
Monarch Butterfly Optimization ◽  
Low Dimensional ◽  
Continuous Optimization Problems

As a significant subset of the family of discrete optimization problems, the 0-1 knapsack problem (0-1 KP) has received considerable attention among the relevant researchers. The monarch butterfly optimization (MBO) is a recent metaheuristic algorithm inspired by the migration behavior of monarch butterflies. The original MBO is proposed to solve continuous optimization problems. This paper presents a novel monarch butterfly optimization with a global position updating operator (GMBO), which can address 0-1 KP known as an NP-complete problem. The global position updating operator is incorporated to help all the monarch butterflies rapidly move towards the global best position. Moreover, a dichotomy encoding scheme is adopted to represent monarch butterflies for solving 0-1 KP. In addition, a specific two-stage repair operator is used to repair the infeasible solutions and further optimize the feasible solutions. Finally, Orthogonal Design (OD) is employed in order to find the most suitable parameters. Two sets of low-dimensional 0-1 KP instances and three kinds of 15 high-dimensional 0-1 KP instances are used to verify the ability of the proposed GMBO. An extensive comparative study of GMBO with five classical and two state-of-the-art algorithms is carried out. The experimental results clearly indicate that GMBO can achieve better solutions on almost all the 0-1 KP instances and significantly outperforms the rest.

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