scholarly journals A Modified Bat Algorithm for Solving Large-Scale Bound Constrained Global Optimization Problems

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Wali Khan Mashwani ◽  
Ihsan Mehmood ◽  
Maharani Abu Bakar ◽  
Ismail Koçcak
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Large Scale ◽  
Impact Analysis ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Stochastic Algorithm ◽  
Optimum Solution ◽  
The Impact ◽  
Modified Bat Algorithm

In the last two decades, the field of global optimization has become very active, and, in this regard, many deterministic and stochastic algorithms were developed for solving various optimization problems. Among them, swarm intelligence (SI) is a stochastic algorithm that is more flexible and robust and has had the ability to find an optimum solution for high-dimensional optimization and search problems. SI-based algorithms are mainly inspired by the social behavior of fish schooling or bird flocking. Among the SI-based algorithms, Bat algorithm (BA) is one of the recently developed evolutionary algorithms. It employs an echolocation behavior of microbats by varying pulse rates of emission and loudness to perform their search process. In this paper, a modified Bat algorithm (MBA) is developed. The main focus of the MBA is to further enhance the exploration and exploitation search abilities of the original Bat algorithm. The performance of the modified Bat algorithm (MBA) is examined over the benchmark functions designed for evolutionary algorithms competition in the special session of 2005 IEEE Congress on Evolutionary Computation. The used benchmark functions include the unimodal, multimodal, and hybrid benchmark functions with high dimensionality. Furthermore, the impact analysis with respect to different values of temperatures is conducted by executing the proposed algorithm twenty-five times independently by using each benchmark function with different random seeds.

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

scholarly journals Modified Bat Algorithm Based on Lévy Flight and Opposition Based Learning

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Xian Shan ◽  
Kang Liu ◽  
Pei-Liang Sun
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Real Life ◽  
Bat Algorithm ◽  
Global Optimum ◽  
Lévy Flight ◽  
Levy Flight ◽  
Local Optima ◽  
Opposition Based Learning ◽  
Modified Bat Algorithm

Bat Algorithm (BA) is a swarm intelligence algorithm which has been intensively applied to solve academic and real life optimization problems. However, due to the lack of good balance between exploration and exploitation, BA sometimes fails at finding global optimum and is easily trapped into local optima. In order to overcome the premature problem and improve the local searching ability of Bat Algorithm for optimization problems, we propose an improved BA called OBMLBA. In the proposed algorithm, a modified search equation with more useful information from the search experiences is introduced to generate a candidate solution, and Lévy Flight random walk is incorporated with BA in order to avoid being trapped into local optima. Furthermore, the concept of opposition based learning (OBL) is embedded to BA to enhance the diversity and convergence capability. To evaluate the performance of the proposed approach, 16 benchmark functions have been employed. The results obtained by the experiments demonstrate the effectiveness and efficiency of OBMLBA for global optimization problems. Comparisons with some other BA variants and other state-of-the-art algorithms have shown the proposed approach significantly improves the performance of BA. Performances of the proposed algorithm on large scale optimization problems and real world optimization problems are not discussed in the paper, and it will be studied in the future work.

scholarly journals Differential Evolution with Novel Mutation and Adaptive Crossover Strategies for Solving Large Scale Global Optimization Problems

2017 ◽  
Vol 2017 ◽  
pp. 1-18 ◽  
Author(s):  
Ali Wagdy Mohamed ◽  
Abdulaziz S. Almazyad
Keyword(s):  
Global Optimization ◽  
Differential Evolution ◽  
Large Scale ◽  
Optimization Problems ◽  
Differential Evolution Algorithm ◽  
High Dimensional ◽  
Gradual Change ◽  
Comparison Results ◽  
Mutation Strategy ◽  
Common Trade

This paper presents Differential Evolution algorithm for solving high-dimensional optimization problems over continuous space. The proposed algorithm, namely, ANDE, introduces a new triangular mutation rule based on the convex combination vector of the triplet defined by the three randomly chosen vectors and the difference vectors between the best, better, and the worst individuals among the three randomly selected vectors. The mutation rule is combined with the basic mutation strategy DE/rand/1/bin, where the new triangular mutation rule is applied with the probability of 2/3 since it has both exploration ability and exploitation tendency. Furthermore, we propose a novel self-adaptive scheme for gradual change of the values of the crossover rate that can excellently benefit from the past experience of the individuals in the search space during evolution process which in turn can considerably balance the common trade-off between the population diversity and convergence speed. The proposed algorithm has been evaluated on the 20 standard high-dimensional benchmark numerical optimization problems for the IEEE CEC-2010 Special Session and Competition on Large Scale Global Optimization. The comparison results between ANDE and its versions and the other seven state-of-the-art evolutionary algorithms that were all tested on this test suite indicate that the proposed algorithm and its two versions are highly competitive algorithms for solving large scale global optimization problems.

A new Kriging–Bat Algorithm for solving computationally expensive black-box global optimization problems

2018 ◽  
Vol 51 (2) ◽  
pp. 265-285 ◽  
Author(s):  
Abdulbaset Saad ◽  
Zuomin Dong ◽  
Brad Buckham ◽  
Curran Crawford ◽  
Adel Younis ◽  
...  
Keyword(s):  
Global Optimization ◽  
Optimization Problems ◽  
Bat Algorithm ◽  
Black Box ◽  
Computationally Expensive

scholarly journals Access to modern energy: a review of barriers, drivers and impacts

2017 ◽  
Vol 22 (5) ◽  
pp. 491-516 ◽  
Author(s):  
Jacopo Bonan ◽  
Stefano Pareglio ◽  
Massimo Tavoni
Keyword(s):  
Time Allocation ◽  
Large Scale ◽  
Impact Analysis ◽  
Universal Access ◽  
Labor Market Outcomes ◽  
Market Outcomes ◽  
Improved Cookstove ◽  
Development Outcomes ◽  
The Impact ◽  
Shed Light

AbstractUniversal access to modern energy services, in terms of access to electricity and to modern cooking facilities, has been recognized as a fundamental challenge for development. Despite strong praise for action and the deployment of large-scale electrification programs and improved cookstove (ICS) distribution campaigns, few studies have shed light on the barriers to, the enablers of and the impacts of access to energy on development outcomes, using rigorous methodologies. This paper reviews this recent strand of research, trying to fill these gaps. The authors focus on the demand-side and household perspective. Their main outcomes of interest are electricity connection and ICS adoption for the analysis of barriers, time allocation, labor market outcomes and welfare for the impact analysis. They provide evidence of significant wellbeing impacts of electrification and mixed evidence for cookstoves.

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