Interactive autodidactic school: A new metaheuristic optimization algorithm for solving mathematical and structural design optimization problems

Jahangiri, Milad; Hadianfard, Mohammad Ali; Najafgholipour, Mohammad Amir; Jahangiri, Mehdi; Gerami, Mohammad Reza

doi:10.1016/j.compstruc.2020.106268

Cited by 50 publications

(15 citation statements)

References 65 publications

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“…An extensive technical background based on meta-heuristics has been presented in the literature in recent years, becoming from the most diverse inspirations, such as the well-known evolutionary algorithms of genetic inspiration [ 23 ], the behavior of students in the classroom [ 24 ], the behavior of different species of animals [ 25 ], and some theoretic-mathematical concepts like the golden ratio [ 26 ]. In particular, some of these techniques have been used successfully in the JSSP solution.…”

Section: Related Workmentioning

confidence: 99%

A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

Viana

Morandin

Contreras

2020

Sensors

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It is not uncommon for today’s problems to fall within the scope of the well-known class of NP-Hard problems. These problems generally do not have an analytical solution, and it is necessary to use meta-heuristics to solve them. The Job Shop Scheduling Problem (JSSP) is one of these problems, and for its solution, techniques based on Genetic Algorithm (GA) form the most common approach used in the literature. However, GAs are easily compromised by premature convergence and can be trapped in a local optima. To address these issues, researchers have been developing new methodologies based on local search schemes and improvements to standard mutation and crossover operators. In this work, we propose a new GA within this line of research. In detail, we generalize the concept of a massive local search operator; we improved the use of a local search strategy in the traditional mutation operator; and we developed a new multi-crossover operator. In this way, all operators of the proposed algorithm have local search functionality beyond their original inspirations and characteristics. Our method is evaluated in three different case studies, comprising 58 instances of literature, which prove the effectiveness of our approach compared to traditional JSSP solution methods.

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Section: Related Workmentioning

confidence: 99%

A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

Viana

Morandin

Contreras

2020

Sensors

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“…Among the other algorithms in this class, most of them endeavor to emulate human intelligence of problem solving for discovering better solutions. A few such algorithms are: Harmony Search [27] (music composition), Brain Storm Optimization [28] (brainstorming sessions), Teacher-Learning Based Optimization [7] (teaching methods), Interactive Autodidactic school (learning methods) [29], Student Psychology Based Optimization (student psychology) [30], and Search Group Algorithm [31] (human search methods).…”

Section: Related Workmentioning

confidence: 99%

“…However, tuning these control parameters is often problem dependent and can sometimes be cumbersome as improper tuning may lead to poor performance. Addressing these issues, several metaheuristics are proposed recently that advocate parameter-free approach, including Interactive Autodidactic School (IAS) [29], Teaching-Learning Based Optimization (TLBO) [7], and Symbiotic Organism Search (SOS) [9].…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Fuzzy Symbiotic Organisms Search Algorithm and Its Applications

2020

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“…et al [14] proposed the M3DDPG method, which can be better adapted to the multi-agent environment. All of the above multi-agent intelligent learning methods have the function of interactive learning, which proves that intelligent learning in multi-agents can achieve self-learning and evolution for the individuals; effectively, it is advanced in learning ability compared with the methods such as DDPG, which concerns only self-learning as an agent [15]. However, these methods lack the support of game theory.…”

Section: Introductionmentioning

confidence: 99%

An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

Zhao

Zhang

et al. 2021

Electronics

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Most current studies on multi-agent evolution based on deep learning take a cooperative equilibrium strategy, while interactive self-learning is not always considered. An interactive self-learning game and evolution method based on non-cooperative equilibrium (ISGE-NCE) is proposed to take the benefits of both game theory and interactive learning for multi-agent confrontation evolution. A generative adversarial network (GAN) is designed combining with multi-agent interactive self-learning, and the non-cooperative equilibrium strategy is well adopted within the framework of interactive self-learning, aiming for high evolution efficiency and interest. For assessment, three typical multi-agent confrontation experiments are designed and conducted. The results show that, first, in terms of training speed, the ISGE-NCE produces a training convergence rate of at least 46.3% higher than that of the method without considering interactive self-learning. Second, the evolution rate of the interference and detection agents reaches 60% and 80%, respectively, after training by using our method. In the three different experiment scenarios, compared with the DDPG, our ISGE-NCE method improves the multi-agent evolution effectiveness by 43.4%, 50%, and 20%, respectively, with low training costs. The performances demonstrate the significant superiority of our ISGE-NCE method in swarm intelligence.

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Interactive autodidactic school: A new metaheuristic optimization algorithm for solving mathematical and structural design optimization problems

Cited by 50 publications

References 65 publications

A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

A Modified Genetic Algorithm with Local Search Strategies and Multi-Crossover Operator for Job Shop Scheduling Problem

An Adaptive Fuzzy Symbiotic Organisms Search Algorithm and Its Applications

An Interactive Self-Learning Game and Evolutionary Approach Based on Non-Cooperative Equilibrium

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