Global search in single-solution-based metaheuristics

2020 ◽  
Vol 54 (3) ◽  
pp. 275-296 ◽  
Author(s):  
Najmeh Sadat Jaddi ◽  
Salwani Abdullah
Keyword(s):  
Search Space ◽  
Population Based ◽  
Global Optimum ◽  
Global Search ◽  
Hill Climbing ◽  
Search Process ◽  
Local Optimum ◽  
Content Type ◽  
Solution Algorithms ◽  
Single Solution

PurposeMetaheuristic algorithms are classified into two categories namely: single-solution and population-based algorithms. Single-solution algorithms perform local search process by employing a single candidate solution trying to improve this solution in its neighborhood. In contrast, population-based algorithms guide the search process by maintaining multiple solutions located in different points of search space. However, the main drawback of single-solution algorithms is that the global optimum may not reach and it may get stuck in local optimum. On the other hand, population-based algorithms with several starting points that maintain the diversity of the solutions globally in the search space and results are of better exploration during the search process. In this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.Design/methodology/approachIn this method, different starting points in initial step, searching locally in neighborhood of each solution, construct a global search in search space for the single-solution algorithm.FindingsThe proposed method was tested based on three single-solution algorithms involving hill-climbing (HC), simulated annealing (SA) and tabu search (TS) algorithms when they were applied on 25 benchmark test functions. The results of the basic version of these algorithms were then compared with the same algorithms integrated with the global search proposed in this paper. The statistical analysis of the results proves outperforming of the proposed method. Finally, 18 benchmark feature selection problems were used to test the algorithms and were compared with recent methods proposed in the literature.Originality/valueIn this paper more chance of finding global optimum is provided for single-solution-based algorithms by searching different regions of the search space.

scholarly journals SOLVING INTEGER PROGRAMMING PROBLEMS BY USING POPULATION-BASED BEETLE ANTENNAE SEARCH ALGORITHM

2021 ◽  
Vol 9 (3-4) ◽  
pp. 89-99
Author(s):  
Ivona Brajević ◽  
Miodrag Brzaković ◽  
Goran Jocić
Keyword(s):  
Integer Programming ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Population Based ◽  
Local Optimum ◽  
Integer Programming Problems ◽  
Metaheuristic Techniques

Beetle antennae search (BAS) algorithm is a newly proposed single-solution based metaheuristic technique inspired by the beetle preying process. Although BAS algorithm has shown good search abilities, it can be easily trapped into local optimum when it is used to solve hard optimization problems. With the intention to overcome this drawback, this paper presents a population-based beetle antennae search (PBAS) algorithm for solving integer programming problems.  This method employs the population's capability to search diverse regions of the search space to provide better guarantee for finding the optimal solution. The PBAS method was tested on nine integer programming problems and one mechanical design problem. The proposed algorithm was compared to other state-of-the-art metaheuristic techniques. The comparisons show that the proposed PBAS algorithm produces better results for majority of tested problems.  

scholarly journals Genetic algorithms - variable size populations of chromosomes. An adaptive approach

2018 ◽  
Vol XIX (1) ◽  
pp. 393-399
Author(s):  
Maniu R
Keyword(s):  
Genetic Algorithms ◽  
Negative Impact ◽  
Large Population ◽  
Search Space ◽  
Global Optimum ◽  
Small Population ◽  
Local Optimum ◽  
Variable Size ◽  
Algorithm Operation

The size of the chromosome population is an essential parameter of genetic algorithms. A large population involves a large amount of calculations but provides a complete scroll of the search space and the increased probability of generating a global optimum. A small population size, through the small number of operations required, causes a quick run of the algorithm, with increasing the probability of detecting a local optimum to the detriment of the global one. This paper proposes the use of an adaptive, variable size of chromosome population. We will demonstrate that this approach leads to an acceleration of the algorithm operation, without having a negative impact on the quality of provided solutions.

scholarly journals An Efficient Tasks Scheduling Algorithm for Distributed Memory Machines With Communication Delays

2010 ◽  
Vol 8 (2) ◽  
pp. 1-16
Author(s):  
Fatma Abd El-Sattar Omara
Keyword(s):  
Heuristic Algorithms ◽  
Scheduling Algorithm ◽  
Complete Solution ◽  
Iterative Algorithms ◽  
Global Optimum ◽  
Hill Climbing ◽  
Multiprocessor System ◽  
Local Optimum ◽  
Communication Delays ◽  
The Hill

The scheduling of multiple interacting tasks of a single parallel program is considered the most important issue to exploit the true performance of the multiprocessor system. The problem is to find a schedule that will minimize the execution time (Make_Span) of a program. On the other hand, task scheduling on a multiprocessor system with and without communication delays is known to be NP-complete problem. Consequently, many heuristic algorithms have been developed, each of which may find optimal scheduling under different circumstances. One of the well known iterative algorithms is the hill-climbing. This algorithm starts with a complete solution and searches to improve this solution by choosing a better neighbor based on a cost function. This will lead to a local optimum which is considered the main drawback of this algorithm. The research in this study concerns to develop an efficient iterative algorithm for scheduling problem based on the hill-climbing. Present algorithm satisfies a local optimum that is very close to the global one in a reasonable amount of time. In most experiments, it satisfies the actual global optimum.

AN IMPROVED POPULATION BASED OPTIMIZATION SOLUTION METHOD BY COMBINING LOCAL AND GLOBAL SEARCHING

2007 ◽  
Vol 16 (05) ◽  
pp. 907-915
Author(s):  
WEI JIANG ◽  
XIAO-LONG WANG ◽  
XIU-LI PANG
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Optimization Problems ◽  
Population Based ◽  
Search Algorithms ◽  
Global Search ◽  
Local Optimum ◽  
Hard Problems ◽  
The Common ◽  
Optimum Solution

Optimization Solution Task is a typical and important task in many applications. Many optimization problems have been proved to be NP-hard problems, which cannot be solved by some predefined mathematic formulae. In this case, computer aided method is very helpful. While some local search algorithms are easily to fall into a local optimum solution. On contrast, the population based methods, such as Genetic Algorithms, Artificial Immune System, Autonomy Oriented Computing, are global search algorithms. However, they are not good at the local search. In this paper, an improved method is proposed by combining the local and global search ability, so as to improve the performance in terms of the convergence speed and the convergence reliability. We construct a generic form to deal with the common objective function space or the objective function with the partial derivative. In addition, we present an n-hold method in population based evolution method. The experiments indicate that our approach can effectively improve the convergence reliability, which is much concerned in some applications with the expensive executing expense.

scholarly journals A Novel Framework for Ab Initio Coarse Protein Structure Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Sandhya Parasnath Dubey ◽  
S. Balaji ◽  
N. Gopalakrishna Kini ◽  
M. Sathish Kumar
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Search Algorithm ◽  
Search Space ◽  
Population Based ◽  
Hill Climbing ◽  
Superior Performance ◽  
Protein Database ◽  
Fitness Value

Hydrophobic-Polar model is a simplified representation of Protein Structure Prediction (PSP) problem. However, even with the HP model, the PSP problem remains NP-complete. This work proposes a systematic and problem specific design for operators of the evolutionary program which hybrids with local search hill climbing, to efficiently explore the search space of PSP and thereby obtain an optimum conformation. The proposed algorithm achieves this by incorporating the following novel features: (i) new initialization method which generates only valid individuals with (rather than random) better fitness values; (ii) use of probability-based selection operators that limit the local convergence; (iii) use of secondary structure based mutation operator that makes the structure more closely to the laboratory determined structure; and (iv) incorporating all the above-mentioned features developed a complete two-tier framework. The developed framework builds the protein conformation on the square and triangular lattice. The test has been performed using benchmark sequences, and a comparative evaluation is done with various state-of-the-art algorithms. Moreover, in addition to hypothetical test sequences, we have tested protein sequences deposited in protein database repository. It has been observed that the proposed framework has shown superior performance regarding accuracy (fitness value) and speed (number of generations needed to attain the final conformation). The concepts used to enhance the performance are generic and can be used with any other population-based search algorithm such as genetic algorithm, ant colony optimization, and immune algorithm.

Fast multi-objective design optimization of microwave and antenna structures using data-driven surrogates and domain segmentation

2019 ◽  
Vol 37 (2) ◽  
pp. 753-788
Author(s):  
Slawomir Koziel ◽  
Adrian Bekasiewicz
Keyword(s):  
Design Optimization ◽  
Design Problem ◽  
Low Cost ◽  
Computational Cost ◽  
Search Space ◽  
Population Based ◽  
Design Framework ◽  
Content Type ◽  
Multi Objective ◽  
Variable Fidelity

Purpose The purpose of this paper is to investigate the strategies and algorithms for expedited design optimization of microwave and antenna structures in multi-objective setup. Design/methodology/approach Formulation of the multi-objective design problem-oriented toward execution of the population-based metaheuristic algorithm within the segmented search space is investigated. Described algorithmic framework exploits variable fidelity modeling, physics- and approximation-based representation of the structure and model correction techniques. The considered approach is suitable for handling various problems pertinent to the design of microwave and antenna structures. Numerical case studies are provided demonstrating the feasibility of the segmentation-based framework for the design of real-world structures in setups with two and three objectives. Findings Formulation of appropriate design problem enables identification of the search space region containing Pareto front, which can be further divided into a set of compartments characterized by small combined volume. Approximation model of each segment can be constructed using a small number of training samples and then optimized, at a negligible computational cost, using population-based metaheuristics. Introduction of segmentation mechanism to multi-objective design framework is important to facilitate low-cost optimization of many-parameter structures represented by numerically expensive computational models. Further reduction of the design cost can be achieved by enforcing equal-volumes of the search space segments. Research limitations/implications The study summarizes recent advances in low-cost multi-objective design of microwave and antenna structures. The investigated techniques exceed capabilities of conventional design approaches involving direct evaluation of physics-based models for determination of trade-offs between the design objectives, particularly in terms of reliability and reduction of the computational cost. Studies on the scalability of segmentation mechanism indicate that computational benefits of the approach decrease with the number of search space segments. Originality/value The proposed design framework proved useful for the rapid multi-objective design of microwave and antenna structures characterized by complex and multi-parameter topologies, which is extremely challenging when using conventional methods driven by population-based metaheuristics algorithms. To the authors knowledge, this is the first work that summarizes segmentation-based approaches to multi-objective optimization of microwave and antenna components.

Genetic Algorithms as Global Random Search Methods: An Alternative Perspective

1995 ◽  
Vol 3 (1) ◽  
pp. 39-80 ◽  
Author(s):  
Charles C. Peck ◽  
Atam P. Dhawan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Random Search ◽  
Schema Theory ◽  
Global Optimum ◽  
Global Search ◽  
Search Process ◽  
Sampling Distributions ◽  
Alternative Perspective ◽  
Global Random Search

Genetic algorithm behavior is described in terms of the construction and evolution of the sampling distributions over the space of candidate solutions. This novel perspective is motivated by analysis indicating that the schema theory is inadequate for completely and properly explaining genetic algorithm behavior. Based on the proposed theory, it is argued that the similarities of candidate solutions should be exploited directly, rather than encoding candidate solutions and then exploiting their similarities. Proportional selection is characterized as a global search operator, and recombination is characterized as the search process that exploits similarities. Sequential algorithms and many deletion methods are also analyzed. It is shown that by properly constraining the search breadth of recombination operators, convergence of genetic algorithms to a global optimum can be ensured.

Naval Hull Form Multicriterion Hydrodynamic Optimization for the Conceptual Design Phase

2009 ◽  
Vol 53 (04) ◽  
pp. 199-213
Author(s):  
Steven F. Zalek ◽  
Michael G. Parsons ◽  
Robert F. Beck
Keyword(s):  
Pareto Front ◽  
Problem Formulation ◽  
Search Space ◽  
Population Based ◽  
Global Optimum ◽  
Optimization Process ◽  
Algorithm Optimization ◽  
Ship Hulls ◽  
Conceptual Design Phase ◽  
Seakeeping Performance

For a given set of ship design and operational criteria, there exists a trade-off between the ship's calm water powering performance and its seakeeping performance. Ship hulls that are designed for optimal powering performance can have poor seakeeping performance, and vice versa. It is typically not possible to obtain the global optimum for both of these objectives because of the competition between the powering and seakeeping performance functions and the design constraints. The set of globally nondominated solutions, or Pareto front, considering these competing criteria is searched for by navigating the multimodal search space using a multicriterion, population-based evolutionary algorithm optimization process. The optimization process uses a nontraditional objective function formulation that eliminates the need to tune the penalty function parameters for each new problem formulation and appears to provide a more thorough representation of the numerically approximated Pareto front. Results show that properly integrating this optimization process with the design criteria yields a set of hydrodynamically superior design solutions. The problem formulation and development is applicable to naval surface vessels and applied to a monohull frigate type example.

Diploidy in evolutionary algorithms for dynamic optimization problems

2015 ◽  
Vol 8 (4) ◽  
pp. 312-329 ◽  
Author(s):  
Boris Shabash ◽  
Kay C. Wiese
Keyword(s):  
Evolutionary Algorithms ◽  
Dynamic Optimization ◽  
Genetic Information ◽  
Optimization Problems ◽  
Fitness Function ◽  
Search Space ◽  
Global Optimum ◽  
Dynamic Optimization Problems ◽  
Content Type ◽  
Optimization Function

Purpose – In this work, the authors show the performance of the proposed diploid scheme (a representation where each individual contains two genotypes) with respect to two dynamic optimization problems, while addressing drawbacks the authors have identified in previous works which compare diploid evolutionary algorithms (EAs) to standard EAs. The paper aims to discuss this issue. Design/methodology/approach – In the proposed diploid representation of EA, each individual possesses two copies of the genotype. In order to convert this pair of genotypes to a single phenotype, each genotype is individually evaluated in relation to the fitness function and the best genotype is presented as the phenotype. In order to provide a fair and objective comparison, the authors make sure to compare populations which contain the same amount of genetic information, where the only difference is the arrangement and interpretation of the information. The two representations are compared using two shifting fitness functions which change at regular intervals to displace the global optimum to a new position. Findings – For small fitness landscapes the haploid (standard) and diploid algorithms perform comparably and are able to find the global optimum very quickly. However, as the search space increases, rediscovering the global optimum becomes more difficult and the diploid algorithm outperforms the haploid algorithm with respect to how fast it relocates the new optimum. Since both algorithms use the same amount of genetic information, it is only fair to conclude it is the unique arrangement of the diploid algorithm that allows it to explore the search space better. Originality/value – The diploid representation presented here is novel in that instead of adopting a dominance scheme for each allele (value) in the vector of values that is the genotype, dominance is adopted across the entire genotype in relation to its homologue. As a result, this representation can be extended across any alphabet, for any optimization function.

scholarly journals Solving integer programming problems by using population-based beetle antennae search algorithm

2021 ◽  
Vol 9 (3-4) ◽  
pp. 89-99
Author(s):  
Ivona Brajević ◽  
Miodrag Brzaković ◽  
Goran Jocić
Keyword(s):  
Integer Programming ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Population Based ◽  
Local Optimum ◽  
Integer Programming Problems ◽  
Metaheuristic Techniques

Beetle antennae search (BAS) algorithm is a newly proposed single-solution based metaheuristic technique inspired by the beetle preying process. Although BAS algorithm has shown good search abilities, it can be easily trapped into local optimum when it is used to solve hard optimization problems. With the intention to overcome this drawback, this paper presents a population-based beetle antennae search (PBAS) algorithm for solving integer programming problems. This method employs the population's capability to search diverse regions of the search space to provide better guarantee for finding the optimal solution. The PBAS method was tested on nine integer programming problems and one mechanical design problem. The proposed algorithm was compared to other state-of-the-art metaheuristic techniques. The comparisons show that the proposed PBAS algorithm produces better results for majority of tested problems.

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