Applying Parallel and Distributed Models on Bio-Inspired Algorithms via a Clustering Method

In the world of optimization, especially concerning metaheuristics, solving complex problems represented by applying big data and constraint instances can be difficult. This is mainly due to the difficulty of implementing efficient solutions that can solve complex optimization problems in adequate time, which do exist in different industries. Big data has demonstrated its efficiency in solving different concerns in information management. In this paper, an approach based on multiprocessing is proposed wherein clusterization and parallelism are used together to improve the search process of metaheuristics when solving large instances of complex optimization problems, incorporating collaborative elements that enhance the quality of the solution. The proposal deals with machine learning algorithms to improve the segmentation of the search space. Particularly, two different clustering methods belonging to automatic learning techniques, are implemented on bio-inspired algorithms to smartly initialize their solution population, and then organize the resolution from the beginning of the search. The results show that this approach is competitive with other techniques in solving a large set of cases of a well-known NP-hard problem without incorporating too much additional complexity into the metaheuristic algorithms.

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Search Patterns Based on Trajectories Extracted from the Response of Second-Order Systems

Applied Sciences ◽

10.3390/app11083430 ◽

2021 ◽

Vol 11 (8) ◽

pp. 3430

Author(s):

Erik Cuevas ◽

Héctor Becerra ◽

Héctor Escobar ◽

Alberto Luque-Chang ◽

Marco Pérez ◽

...

Keyword(s):

Convergence Rates ◽

Optimization Problems ◽

Search Space ◽

Second Order ◽

Order System ◽

Search Patterns ◽

Multiple Behaviors ◽

Complex Optimization ◽

Second Order Systems ◽

Order Algorithm

Recently, several new metaheuristic schemes have been introduced in the literature. Although all these approaches consider very different phenomena as metaphors, the search patterns used to explore the search space are very similar. On the other hand, second-order systems are models that present different temporal behaviors depending on the value of their parameters. Such temporal behaviors can be conceived as search patterns with multiple behaviors and simple configurations. In this paper, a set of new search patterns are introduced to explore the search space efficiently. They emulate the response of a second-order system. The proposed set of search patterns have been integrated as a complete search strategy, called Second-Order Algorithm (SOA), to obtain the global solution of complex optimization problems. To analyze the performance of the proposed scheme, it has been compared in a set of representative optimization problems, including multimodal, unimodal, and hybrid benchmark formulations. Numerical results demonstrate that the proposed SOA method exhibits remarkable performance in terms of accuracy and high convergence rates.

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Hybrid Simulated Annealing with Meta-Heuristic Methods to Solve UCT Problem

Modern Applied Science ◽

10.5539/mas.v12n11p366 ◽

2018 ◽

Vol 12 (11) ◽

pp. 366 ◽

Cited By ~ 1

Author(s):

Issam AlHadid ◽

Khalid Kaabneh ◽

Hassan Tarawneh

Keyword(s):

Simulated Annealing ◽

Optimization Problems ◽

Search Space ◽

Local Optimum ◽

Solution Quality ◽

University Course Timetabling ◽

Hybrid Simulated Annealing ◽

Complex Optimization ◽

List Memory ◽

University Course

Simulated Annealing (SA) is a common meta-heuristic algorithm that has been widely used to solve complex optimization problems. This work proposes a hybrid SA with EMC to divert the search effectively to another promising region. Moreover, a Tabu list memory applied to avoid cycling. Experimental results showed that the solution quality has enhanced using SA-EMCQ by escaping the search space from local optimum to another promising region space. In addition, the results showed that our proposed technique has outperformed the standard SA and gave comparable results to other approaches in the literature when tested on ITC2007-Track3 university course timetabling datasets.

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A New Multi-Objective Firework Algorithm to Solve the Multimodal Planning Network Problem

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2020100105 ◽

2020 ◽

Vol 11 (4) ◽

pp. 91-113

Author(s):

Mouna Gargouri Mnif ◽

Sadok Bouamama

Keyword(s):

Optimization Problems ◽

Transportation Network ◽

Search Space ◽

Search Technique ◽

Multimodal Transportation ◽

New Approach ◽

Multi Objective ◽

Network Problem ◽

Complex Optimization ◽

Stochastic Search Technique

This article introduces a new approach called multi-objective firework algorithm (MFWA). The proposed approach allows for solving the multimodal transportation network problem (MTNP). The main goal is to develop a decision system that optimizes and determines the planning network of the multimodal transportation (PNMT) problem. The optimization involves reaching the efficient transport mode and multimodal path, in order to move from one country to another while satisfying the set of objectives. Moreover, the firework algorithm has distinct advantages in solving complex optimization problems and in obtaining a solution by a distributed and oriented research system. This approach presents a search way, which is different from the swarm intelligence-based stochastic search technique. For each firework, the process starts by exploding a firework in the sky. The search space is filled with a shower of sparks to get diversity solutions. This new approach proves their efficacy in solving the multi-objective problem, which is shown by the experimental results.

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Catastrophe-Based Antibody Clone Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.121-126.4415 ◽

2011 ◽

Vol 121-126 ◽

pp. 4415-4420

Author(s):

Yu Zhang ◽

Li Hua Wu ◽

Zi Qiang Luo

Keyword(s):

Optimization Problems ◽

Optimization Algorithms ◽

Search Space ◽

Natural World ◽

Population Diversity ◽

Selection Algorithm ◽

Intelligent Optimization Algorithms ◽

Complex Optimization ◽

Search Capability ◽

Better Than

In solving complex optimization problems, intelligent optimization algorithms such as immune algorithm show better advantages than traditional optimization algorithms. Most of these immune algorithms, however, have disadvantages in population diversity and preservation of elitist antibodies genes, which will lead to the degenerative phenomenon, the zigzag phenomenon, poor global optimization, and low convergence speed. By introducing the catastrophe factor into the ACAMHC algorithm, we propose a novel catastrophe-based antibody clone algorithm (CACA) to solve the above problems. CACA preserves elitist antibody genes through the vaccine library to improve its local search capability; it improves the antibody population diversity by gene mutation that mimics the catastrophe events to the natural world to enhance its global search capability. To expand the antibody search space, CACA will add some new random immigrant antibodies with a certain ratio. The convergence of CACA is theoretically proved. The experiments of CACA compared with the clone selection algorithm (ACAMHC) on some benchmark functions are carried out. The experimental results indicate that the performance of CACA is better than that of ACAMHC. The CACA algorithm provides new opportunities for solving previously intractable optimization problems.

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MHC-INSPIRED ANTIBODY CLONE ALGORITHM

International Journal of Computational Methods ◽

10.1142/s0219876210002179 ◽

2010 ◽

Vol 07 (02) ◽

pp. 299-318 ◽

Cited By ~ 2

Author(s):

YU ZHANG ◽

XUMING CHEN ◽

LIHUA WU ◽

ZIQIANG LUO ◽

XIAOJIE LIU

Keyword(s):

Optimization Problems ◽

Optimization Algorithms ◽

Search Space ◽

Population Diversity ◽

Histocompatibility Complex ◽

Intelligent Optimization Algorithms ◽

Mhc Polymorphism ◽

Mhc Haplotype ◽

Complex Optimization ◽

Search Capability

For solving complex optimization problems in some engineering applications, intelligent optimization algorithms based on biological mechanisms have better performance than traditional optimization algorithms. Most of these intelligent algorithms, however, have disadvantages in population diversity and preservation of elitist antibody genes, which lead to the degenerative phenomenon, the zigzag phenomenon, poor global optimization, and low convergence speed. Drawing inspiration from the features of major histocompatibility complex (MHC) in the biological immune system, we propose a novel MHC-inspired antibody clone algorithm (ACAMHC) for solving the above problems. ACAMHC preserves elitist antibody genes through the MHC strings that emulate the MHC haplotype in order to improve its local search capability; it improves the antibody population diversity by gene mutation that mimick the MHC polymorphism to enhance its global search capability. To expand the antibody search space, ACAMHC will add some new random immigrant antibodies with a certain ratio. The convergence of ACAMHC is theoretically proven. The experiments of ACAMHC compared with the canonical clone selection algorithm (CLONALG) on 20 benchmark functions are carried out. The experimental results indicate that the performance of ACAMHC is better than that of CLONALG. The ACAMHC algorithm provides new opportunities for solving previously intractable optimization problems.

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