Wild Goats Algorithm: An Evolutionary Algorithm to Solve the Real-World Optimization 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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CMCABC: Clustering and Memory-Based Chaotic Artificial Bee Colony Dynamic Optimization Algorithm

International Journal of Information Technology & Decision Making ◽

10.1142/s0219622018500153 ◽

2018 ◽

Vol 17 (04) ◽

pp. 1007-1046 ◽

Cited By ~ 10

Author(s):

Mohsen Moradi ◽

Samad Nejatian ◽

Hamid Parvin ◽

Vahideh Rezaie

Keyword(s):

Dynamic Optimization ◽

Real World ◽

Artificial Bee Colony ◽

Optimization Problems ◽

Optimization Algorithms ◽

Dynamic Environments ◽

Dynamic Optimization Problems ◽

The Real ◽

Bee Colony ◽

Swarm Intelligence Optimization

The swarm intelligence optimization algorithms are used widely in static purposes and applications. They solve the static optimization problems successfully. However, most of the recent optimization problems in the real world have a dynamic nature. Thus, an optimization algorithm is required to solve the problems in dynamic environments as well. The dynamic optimization problems indicate the ones whose solutions change over time. The artificial bee colony algorithm is one of the swarm intelligence optimization algorithms. In this study, a clustering and memory-based chaotic artificial bee colony algorithm, denoted by CMCABC, has been proposed for solving the dynamic optimization problems. A chaotic system has a more accurate prediction for future in the real-world applications compared to a random system, because in the real-world chaotic behaviors have emerged, but random behaviors havenot been observed. In the proposed CMCABC method, explicit memory has been used to save the previous good solutions which are not very old. Maintaining diversity in the dynamic environments is one of the fundamental challenges while solving the dynamic optimization problems. Using clustering technique in the proposed method can well maintain the diversity of the problem environment. The proposed CMCABC method has been tested on the moving peaks benchmark (MPB). The MPB is a good simulator to evaluate the efficiency of the optimization algorithms in dynamic environments. The experimental results on the MPB reveal the appropriate efficiency of the proposed CMCABC method compared to the other state-of-the-art methods in solving dynamic optimization problems.

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A New Evolutionary Algorithm: Learner Performance Based Behavior Algorithm

10.36227/techrxiv.12922127 ◽

2020 ◽

Author(s):

Chnoor M. Rahman ◽

Tarik A. Rashid

Keyword(s):

High School ◽

Evolutionary Algorithm ◽

Real World ◽

Optimization Problems ◽

Initial Population ◽

Test Functions ◽

Great Ability ◽

Learner Performance ◽

Global Optima

A novel evolutionary algorithm called learner performance based behavior algorithm (LPB) is proposed in this article. The basic inspiration of LPB originates from the process of accepting graduated learners from high school in different departments at university. In addition, the changes those learners should do in their studying behaviors to improve their study level at university. The most important stages of optimization; exploitation and exploration are outlined by designing the process of accepting graduated learners from high school to university and the procedure of improving the learner’s studying behavior at university to improve the level of their study. To show the accuracy of the proposed algorithm, it is evaluated against a number of test functions, such as traditional benchmark functions, CEC-C06 2019 test functions, and a real-world case study problem. The results of the proposed algorithm are then compared to the DA, GA, and PSO. The proposed algorithm produced superior results in most of the cases and comparative in some others. It is proved that the algorithm has a great ability to deal with the large optimization problems comparing to the DA, GA, and PSO. The overall results proved the ability of LPB in improving the initial population and converging towards the global optima. Moreover, the results of the proposed work are proved statistically. <br>

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An Ensemble Framework of Evolutionary Algorithm for Constrained Multi-Objective Optimization

Symmetry ◽

10.3390/sym14010116 ◽

2022 ◽

Vol 14 (1) ◽

pp. 116

Author(s):

Junhua Ku ◽

Fei Ming ◽

Wenyin Gong

Keyword(s):

Evolutionary Algorithms ◽

Real World ◽

Optimization Problems ◽

State Of The Art ◽

Multi Objective Optimization ◽

The Real ◽

Multi Objective ◽

The Past ◽

Hypervolume Indicator ◽

Real World Problems

In the real-world, symmetry or asymmetry widely exists in various problems. Some of them can be formulated as constrained multi-objective optimization problems (CMOPs). During the past few years, handling CMOPs by evolutionary algorithms has become more popular. Lots of constrained multi-objective optimization evolutionary algorithms (CMOEAs) have been proposed. Whereas different CMOEAs may be more suitable for different CMOPs, it is difficult to choose the best one for a CMOP at hand. In this paper, we propose an ensemble framework of CMOEAs that aims to achieve better versatility on handling diverse CMOPs. In the proposed framework, the hypervolume indicator is used to evaluate the performance of CMOEAs, and a decreasing mechanism is devised to delete the poorly performed CMOEAs and to gradually determine the most suitable CMOEA. A new CMOEA, namely ECMOEA, is developed based on the framework and three state-of-the-art CMOEAs. Experimental results on five benchmarks with totally 52 instances demonstrate the effectiveness of our approach. In addition, the superiority of ECMOEA is verified through comparisons to seven state-of-the-art CMOEAs. Moreover, the effectiveness of ECMOEA on the real-world problems is also evaluated for eight instances.

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A New Evolutionary Algorithm: Learner Performance Based Behavior Algorithm

10.36227/techrxiv.12922127.v1 ◽

2020 ◽

Author(s):

Chnoor M. Rahman ◽

Tarik A. Rashid

Keyword(s):

High School ◽

Evolutionary Algorithm ◽

Real World ◽

Optimization Problems ◽

Initial Population ◽

Test Functions ◽

Great Ability ◽

Learner Performance ◽

Global Optima

A novel evolutionary algorithm called learner performance based behavior algorithm (LPB) is proposed in this article. The basic inspiration of LPB originates from the process of accepting graduated learners from high school in different departments at university. In addition, the changes those learners should do in their studying behaviors to improve their study level at university. The most important stages of optimization; exploitation and exploration are outlined by designing the process of accepting graduated learners from high school to university and the procedure of improving the learner’s studying behavior at university to improve the level of their study. To show the accuracy of the proposed algorithm, it is evaluated against a number of test functions, such as traditional benchmark functions, CEC-C06 2019 test functions, and a real-world case study problem. The results of the proposed algorithm are then compared to the DA, GA, and PSO. The proposed algorithm produced superior results in most of the cases and comparative in some others. It is proved that the algorithm has a great ability to deal with the large optimization problems comparing to the DA, GA, and PSO. The overall results proved the ability of LPB in improving the initial population and converging towards the global optima. Moreover, the results of the proposed work are proved statistically. <br>

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A Constrained Many-objective Optimization Evolutionary Algorithm with Enhanced Mating and Environmental Selections

10.36227/techrxiv.14999400.v1 ◽

2021 ◽

Author(s):

Fei Ming

Keyword(s):

Evolutionary Algorithms ◽

Evolutionary Algorithm ◽

Density Estimation ◽

Real World ◽

Selection Pressure ◽

Optimization Problems ◽

Variable Number ◽

Objective Functions ◽

Considerable Research ◽

Real World Applications

<div>Unlike the considerable research on solving many objective optimization problems with evolutionary algorithms, there has been much less research on constrained many-objective optimization problems (CMaOPs). Generally, to effectively solve CMaOPs, an algorithm needs to balance feasibility, convergence, and diversity simultaneously. It is essential for handling CMaOPs yet most of the existing research encounters difficulties. This paper proposes a novel constrained many-objective optimization evolutionary algorithm with enhanced mating and environmental selections, namely CMME. The main features are: i) two ranking strategies are proposed and applied in the mating and environmental selections to enrich feasibility and convergence; ii) an individual density estimation is designed, and crowding distance is integrated to promote diversity; and iii) the ?-dominance is used to strengthen the selection pressure on both the convergence and diversity. The synergy of these components can achieve the goal of balancing feasibility, convergence, and diversity for solving CMaOPs. The proposed CMME algorithm is evaluated on 10 CMaOPs with different features and a variable number of objective functions. Experimental results on three benchmark CMOPs and three real-world applications demonstrate that CMME shows superiority or competitiveness over nine related algorithms.</div>

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A Constrained Many-objective Optimization Evolutionary Algorithm with Enhanced Mating and Environmental Selections

10.36227/techrxiv.14999400 ◽

2021 ◽

Author(s):

Fei Ming

Keyword(s):

Evolutionary Algorithms ◽

Evolutionary Algorithm ◽

Density Estimation ◽

Real World ◽

Selection Pressure ◽

Optimization Problems ◽

Variable Number ◽

Objective Functions ◽

Considerable Research ◽

Real World Applications

<div>Unlike the considerable research on solving many objective optimization problems with evolutionary algorithms, there has been much less research on constrained many-objective optimization problems (CMaOPs). Generally, to effectively solve CMaOPs, an algorithm needs to balance feasibility, convergence, and diversity simultaneously. It is essential for handling CMaOPs yet most of the existing research encounters difficulties. This paper proposes a novel constrained many-objective optimization evolutionary algorithm with enhanced mating and environmental selections, namely CMME. The main features are: i) two ranking strategies are proposed and applied in the mating and environmental selections to enrich feasibility and convergence; ii) an individual density estimation is designed, and crowding distance is integrated to promote diversity; and iii) the ?-dominance is used to strengthen the selection pressure on both the convergence and diversity. The synergy of these components can achieve the goal of balancing feasibility, convergence, and diversity for solving CMaOPs. The proposed CMME algorithm is evaluated on 10 CMaOPs with different features and a variable number of objective functions. Experimental results on three benchmark CMOPs and three real-world applications demonstrate that CMME shows superiority or competitiveness over nine related algorithms.</div>

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A test-suite of non-convex constrained optimization problems from the real-world and some baseline results

Swarm and Evolutionary Computation ◽

10.1016/j.swevo.2020.100693 ◽

2020 ◽

Vol 56 ◽

pp. 100693 ◽

Cited By ~ 12

Author(s):

Abhishek Kumar ◽

Guohua Wu ◽

Mostafa Z. Ali ◽

Rammohan Mallipeddi ◽

Ponnuthurai Nagaratnam Suganthan ◽

...

Keyword(s):

Constrained Optimization ◽

Real World ◽

Optimization Problems ◽

Test Suite ◽

Constrained Optimization Problems ◽

The Real

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Hybridizing Harmony Search Algorithm with Multi-Parent Crossover to Solve Real World Optimization Problems

International Journal of Applied Metaheuristic Computing ◽

10.4018/ijamc.2013070101 ◽

2013 ◽

Vol 4 (3) ◽

pp. 1-14 ◽

Cited By ~ 5

Author(s):

Iyad Abu Doush ◽

Faisal Alkhateeb ◽

Eslam Al Maghayreh ◽

Mohammed Azmi Al-Betar ◽

Basima Hani F. Hasan

Keyword(s):

Evolutionary Algorithm ◽

Real World ◽

Numerical Optimization ◽

Optimization Problems ◽

Search Algorithm ◽

Harmony Search ◽

Harmony Search Algorithm ◽

Experimental Results ◽

Test Problems ◽

Harmony Memory

Harmony search algorithm (HSA) is a recent evolutionary algorithm used to solve several optimization problems. The algorithm mimics the improvisation behaviour of a group of musicians to find a good harmony. Several variations of HSA have been proposed to enhance its performance. In this paper, a new variation of HSA that uses multi-parent crossover is proposed (HSA-MPC). In this technique three harmonies are used to generate three new harmonies that will replace the worst three solution vectors in the harmony memory (HM). The algorithm has been applied to solve a set of eight real world numerical optimization problems (1-8) introduced for IEEE-CEC2011 evolutionary algorithm competition. The experimental results of the proposed algorithm are compared with the original HSA, and two variations of HSA: global best HSA and tournament HSA. The HSA-MPC almost always shows superiority on all test problems.

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