Solving design optimization problems via hunting search algorithm with Levy flights

2014 ◽  
Vol 52 (2) ◽  
pp. 351-368 ◽  
Author(s):  
Erkan Dogan
Keyword(s):  
Design Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Lévy Flights ◽  
Levy Flights ◽  
Hunting Search Algorithm

scholarly journals Determining Optimal Link Capacity Expansions in Road Networks Using Cuckoo Search Algorithm with Lévy Flights

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Ozgur Baskan
Keyword(s):  
Traffic Congestion ◽  
Road Network ◽  
Search Algorithm ◽  
Road Networks ◽  
Cuckoo Search ◽  
User Equilibrium ◽  
Network Design Problem ◽  
Lévy Flights ◽  
Link Capacity ◽  
Levy Flights

During the last two decades, Continuous Network Design Problem (CNDP) has received much more attention because of increasing trend of traffic congestion in road networks. In the CNDP, the problem is to find optimal link capacity expansions by minimizing the sum of total travel time and investment cost of capacity expansions in a road network. Considering both increasing traffic congestion and limited budgets of local authorities, the CNDP deserves to receive more attention in order to use available budget economically and to mitigate traffic congestion. The CNDP can generally be formulated as bilevel programming model in which the upper level deals with finding optimal link capacity expansions, whereas at the lower level, User Equilibrium (UE) link flows are determined by Wardrop’s first principle. In this paper, cuckoo search (CS) algorithm with Lévy flights is introduced for finding optimal link capacity expansions because of its recent successful applications in solving such complex problems. CS is applied to the 16-link and Sioux Falls networks and compared with available methods in the literature. Results show the potential of CS for finding optimal or near optimal link capacity expansions in a given road network.

scholarly journals Cuckoo Search Algorithm with Lévy Flights for Global-Support Parametric Surface Approximation in Reverse Engineering

Symmetry ◽  
2018 ◽  
Vol 10 (3) ◽  
pp. 58 ◽  
Author(s):  
Andrés Iglesias ◽  
Akemi Gálvez ◽  
Patricia Suárez ◽  
Mikio Shinya ◽  
Norimasa Yoshida ◽  
...  
Keyword(s):  
Reverse Engineering ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Parametric Surface ◽  
Lévy Flights ◽  
Surface Approximation ◽  
Levy Flights

Improving Cuckoo Search Algorithm With Mittag-Leffler Distribution

2019 ◽  
Author(s):  
Jiamin Wei ◽  
YangQuan Chen ◽  
Yongguang Yu ◽  
Yuquan Chen
Keyword(s):  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Cuckoo Search Algorithm ◽  
Lévy Flights ◽  
Lévy Distribution ◽  
Levy Flights ◽  
Levy Distribution ◽  
Heavy Tailed ◽  
The Impact

Abstract Cuckoo search (CS), as one of the recent nature-inspired metaheuristic algorithms, has proved to be an efficient approach due to the combination of Lévy flights, local search capabilities and guaranteed global convergence. CS uses Lévy flights in global random walk to explore the search space. The Lévy step is taken from the Lévy distribution which is a heavy-tailed probability distribution. In this case, a fraction of large steps are generated, which plays an important role in enhancing search capability of CS. Besides, although many foragers and wandering animals have been shown to follow a Lévy distribution of steps, investigation into the impact of other different heavy-tailed probability distributions on CS is still insufficient up to now. Based on the above considerations, we are motivated to apply the well-known Mittag-Leffler distribution to the standard CS algorithm, and proposed an improved cuckoo search algorithm (CSML) in this paper, where a more efficient search is supposed to take place in the search space thanks to the long jumps. In order to verify the performance of CSML, experiments are carried out on a test suite of 20 benchmark functions. In terms of the observations and results analysis, CSML can be regarded as a new potentially promising algorithm for solving optimization problems.

scholarly journals Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
P. Sabarinath ◽  
M. R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Optimal Design ◽  
Multiobjective Optimization ◽  
Design Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Production Costs ◽  
Machining Parameters ◽  
Conflicting Objectives

The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.

scholarly journals Improved Hunting Search Algorithm for the Quadratic Assignment Problem

2019 ◽  
Vol 14 (1) ◽  
pp. 143 ◽  
Author(s):  
Amine Agharghor ◽  
Mohammed Essaid Riffi ◽  
Fayçal Chebihi
Keyword(s):  
Assignment Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Quadratic Assignment Problem ◽  
Continuous Optimization ◽  
Quadratic Assignment ◽  
Combinatorial Optimization Problems ◽  
Group Hunting ◽  
Continuous Optimization Problems ◽  
Hunting Search Algorithm

<p>Nowadays, the metaheuristics are the most studied methods used to solve the hard optimization problems. Hunting Search algorithm is a metaheuristic inspired by the method of group hunting of predatory animals like wolves. Created for solving continuous optimization problems, recently, it is adapted and evaluated to solve hard combinatorial optimization problems. This paper proposes an improved hunting search algorithm to solve the quadratic assignment problem. No local search method is used. To evaluate the performances of this work, the improved Hunting Search is checked on a set of 36 instances of QAPLib and it outperforms the results obtained by the well-known metaheuristics.</p>

Comparison of Meta-heuristic Algorithms on Benchmark Functions

2019 ◽  
Vol 2 (3) ◽  
pp. 508-517
Author(s):  
FerdaNur Arıcı ◽  
Ersin Kaya
Keyword(s):  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Differential Evolution Algorithm ◽  
Population Based ◽  
Time Interval ◽  
Bee Colony ◽  
Different Characteristics

Optimization is a process to search the most suitable solution for a problem within an acceptable time interval. The algorithms that solve the optimization problems are called as optimization algorithms. In the literature, there are many optimization algorithms with different characteristics. The optimization algorithms can exhibit different behaviors depending on the size, characteristics and complexity of the optimization problem. In this study, six well-known population based optimization algorithms (artificial algae algorithm - AAA, artificial bee colony algorithm - ABC, differential evolution algorithm - DE, genetic algorithm - GA, gravitational search algorithm - GSA and particle swarm optimization - PSO) were used. These six algorithms were performed on the CEC&amp;rsquo;17 test functions. According to the experimental results, the algorithms were compared and performances of the algorithms were evaluated.

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