Comparison of evolutionary-based optimization algorithms for structural design optimization
01 Jan 2013-Engineering Applications of Artificial Intelligence (Pergamon)-Vol. 26, Iss: 1, pp 327-333
TL;DR: The results show that the proposed approach gives better solutions compared to genetic algorithm, particle swarm, immune algorithm, artificial bee colony algorithm and differential evolution algorithm that are representative of the state-of-the-art in the evolutionary optimization literature.
About: This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2013-01-01. It has received 256 citations till now. The article focuses on the topics: Meta-optimization & Multi-swarm optimization.
Citations
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TL;DR: The experimental results for large-sized problems from a large set of randomly generated graphs as well as graphs of real-world problems with various characteristics show that the proposed MPQGA algorithm outperforms two non-evolutionary heuristics and a random search method in terms of schedule quality.
305 citations
Cites background from "Comparison of evolutionary-based op..."
...It is well known that particle swarm optimization (PSO) [13–17], ant colony optimization (ACO) [18,19], artificial bee colony algorithm (ABC) [20], simulated annealing (SA) [21,22], cuckoo search algorithm (CS) [23], tabu search (TS) [24,25], evolution algorithm [26,27], and genetic algorithms (GA) [24,28–42] have been successfully applied to the scheduling....
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01 Nov 2014
TL;DR: A new nature-inspired metaheuristic algorithm is proposed to solve the optimal power flow problem in a power system inspired by the black hole phenomenon and seems to be a promising alternative for solving optimal powerflow problems.
Abstract: We solved the optimal power flow for different cases and different test systems.We used a new approach which is the black-hole-based optimization approach (BHBO).The efficiency of the BHBO has been proven by carrying out a comparative and statistical studies.BHBO is conceptually very simple, further unlike other optimization techniques BHBO parameter-less optimization technique. In this paper a new nature-inspired metaheuristic algorithm is proposed to solve the optimal power flow problem in a power system. This algorithm is inspired by the black hole phenomenon. A black hole is a region of space-time whose gravitational field is so strong that nothing which enters it, not even light, can escape. The developed approach is called black-hole-based optimization approach. In order to show the effectiveness of the proposed approach, it has been demonstrated on the standard IEEE 30-bus test system for different objectives. Furthermore, in order to demonstrate the scalability and suitability of the proposed approach for large-scale and real power systems, it has been tested on the real Algerian 59-bus power system network. The results obtained are compared with those of other methods reported in the literature. Considering the simplicity of the proposed approach and the quality of the obtained results, this approach seems to be a promising alternative for solving optimal power flow problems.
190 citations
01 Nov 2015
TL;DR: An adaptive FA is proposed in this paper to solve mechanical design optimization problems, and the adaptivity is focused on the search mechanism and adaptive parameter settings.
Abstract: Proposing an extension of firefly algorithmEmployment of picewise chaos, for an further enhanced diversityMaking use of a simple but effective constraint handling methodMaking use of an improved local search procedure Firefly algorithm (FA) is a newer member of bio-inspired meta-heuristics, which was originally proposed to find solutions to continuous optimization problems Popularity of FA has increased recently due to its effectiveness in handling various optimization problems To enhance the performance of the FA even further, an adaptive FA is proposed in this paper to solve mechanical design optimization problems, and the adaptivity is focused on the search mechanism and adaptive parameter settings Moreover, chaotic maps are also embedded into AFA for performance improvement It is shown through experimental tests that some of the best known results are improved by the proposed algorithm
189 citations
TL;DR: Experimental results indicate that the proposed cuckoo search algorithm performs better than, or at least comparable to state-of-the-art methods from literature when considering the quality of the solutions obtained.
185 citations
Cites background from "Comparison of evolutionary-based op..."
...In the past decade, we have viewed different kinds of evolutionary algorithms advanced to solve optimization problems, such as genetic algorithm (GA), particle swarm optimization algorithm (PSO), estimation of distribution algorithms (EDA), ant colony optimization (ACO), krill herd algorithm (KHA), biogeography based optimization (BBO), differential evolution (DE), artificial bee colony (ABC), and cuckoo search algorithm (CS) [5,14,17,20,35–40]....
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TL;DR: Three modified versions of the symbiotic organisms search algorithm are proposed by introducing adaptive benefit factors in the basic SOS algorithm to improve its efficiency and reveal that the adaptive SOS algorithm is more reliable and efficient than thebasic SOS algorithm and other state-of-the-art algorithms.
181 citations
Cites background from "Comparison of evolutionary-based op..."
...Discrete structural optimization is also known as truss optimization and having connectivity of finite dimension parameters as variables (naturally discrete parameter system) and continuum structural optimization have field as a variable (discretized parameter system) [1,28,3,46,48]....
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References
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TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
Abstract: A new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented. By means of an extensive testbed it is demonstrated that the new method converges faster and with more certainty than many other acclaimed global optimization methods. The new method requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
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TL;DR: Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm that is used for optimizing multivariable functions and the results showed that ABC outperforms the other algorithms.
Abstract: Swarm intelligence is a research branch that models the population of interacting agents or swarms that are able to self-organize. An ant colony, a flock of birds or an immune system is a typical example of a swarm system. Bees' swarming around their hive is another example of swarm intelligence. Artificial Bee Colony (ABC) Algorithm is an optimization algorithm based on the intelligent behaviour of honey bee swarm. In this work, ABC algorithm is used for optimizing multivariable functions and the results produced by ABC, Genetic Algorithm (GA), Particle Swarm Algorithm (PSO) and Particle Swarm Inspired Evolutionary Algorithm (PS-EA) have been compared. The results showed that ABC outperforms the other algorithms.
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