Learning automata-based butterfly optimization algorithm for engineering design problems

2018 ◽  
Vol 07 (04) ◽  
pp. 1850021 ◽  
Author(s):  
Sankalap Arora ◽  
Priyanka Anand
Keyword(s):  
Engineering Design ◽  
Optimization Algorithm ◽  
Random Search ◽  
Learning Automata ◽  
Global Optimum ◽  
Design Problems ◽  
Local Optima ◽  
Learning Automaton ◽  
Engineering Design Problems ◽  
Benchmark Test Functions

Butterfly Optimization Algorithm (BOA) is a novel meta-heuristic algorithm inspired by the food foraging behavior of the butterflies. The performance of BOA critically depends upon the probability parameter which decides whether the butterfly has to move towards the best butterfly of the population or perform a random search. Therefore, in order to increase the potential of the BOA, which focuses on exploration phase in the initial stages and on exploitation in the later stages of the optimization, learning automata have been embedded in BOA in which a learning automaton takes the role of configuring the behavior of a butterfly in order to create a proper balance between the process of global and local search. The introduction of learning automata accelerates the global convergence speed to the true global optimum while preserving the main feature of the basic BOA. In order to validate the effectiveness of the proposed algorithm, it is evaluated on 17 benchmark test functions and 3 classical engineering design problems with different characteristics, having real-world applications. The simulation results demonstrate that the introduction of learning automata in BOA has significantly boosted the performance of BOA in terms of achievement of true global optimum and avoidance of local optima entrapment.

scholarly journals An improved arithmetic optimization algorithm with forced switching mechanism for global optimization problems

2022 ◽  
Vol 19 (1) ◽  
pp. 473-512
Author(s):  
Rong Zheng ◽  
◽  
Heming Jia ◽  
Laith Abualigah ◽  
Qingxin Liu ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Heuristic Method ◽  
Population Diversity ◽  
Test Functions ◽  
Design Problems ◽  
Local Optima ◽  
Switching Mechanism ◽  
Engineering Design Problems

<abstract> <p>Arithmetic optimization algorithm (AOA) is a newly proposed meta-heuristic method which is inspired by the arithmetic operators in mathematics. However, the AOA has the weaknesses of insufficient exploration capability and is likely to fall into local optima. To improve the searching quality of original AOA, this paper presents an improved AOA (IAOA) integrated with proposed forced switching mechanism (FSM). The enhanced algorithm uses the random math optimizer probability (<italic>RMOP</italic>) to increase the population diversity for better global search. And then the forced switching mechanism is introduced into the AOA to help the search agents jump out of the local optima. When the search agents cannot find better positions within a certain number of iterations, the proposed FSM will make them conduct the exploratory behavior. Thus the cases of being trapped into local optima can be avoided effectively. The proposed IAOA is extensively tested by twenty-three classical benchmark functions and ten CEC2020 test functions and compared with the AOA and other well-known optimization algorithms. The experimental results show that the proposed algorithm is superior to other comparative algorithms on most of the test functions. Furthermore, the test results of two training problems of multi-layer perceptron (MLP) and three classical engineering design problems also indicate that the proposed IAOA is highly effective when dealing with real-world problems.</p> </abstract>

Nonlinear Mixed Integer-Discrete-Continuous Programming and its Application to Engineering Design Problems

1989 ◽  
Author(s):  
J.-F. Fu ◽  
R. G. Fenton ◽  
W. L. Cleghorn
Keyword(s):  
Nonlinear Programming ◽  
Objective Function ◽  
Engineering Design ◽  
Optimization Algorithm ◽  
Mixed Integer ◽  
Continuous Variables ◽  
Design Problems ◽  
Continuous Programming ◽  
Engineering Design Problems ◽  
Standard Values

Abstract An algorithm for solving nonlinear programming problems containing integer, discrete and continuous variables is presented. Based on a commonly employed optimization algorithm, penalties on integer and/or discrete violations are imposed on the objective function to force the search to converge onto standard values. Examples are included to illustrate the practical use of this algorithm.

Swarm Intelligence for Multi-Objective Optimization in Engineering Design

2018 ◽  
pp. 239-250 ◽  
Author(s):  
Janga Reddy Manne
Keyword(s):  
Swarm Intelligence ◽  
Engineering Design ◽  
Random Search ◽  
Search Space ◽  
Population Based ◽  
Design Problems ◽  
Multi Objective ◽  
Hard Constraints ◽  
Decision Variables ◽  
Engineering Design Problems

Most of the engineering design problems are intrinsically complex and difficult to solve, because of diverse solution search space, complex functions, continuous and discrete nature of decision variables, multiple objectives and hard constraints. Swarm intelligence (SI) algorithms are becoming popular in dealing with these kind of complexities. The SI algorithms being population based random search techniques, use heuristics inspired from nature to enable effective exploration of optimal solutions to complex engineering problems. The SI algorithms derived based on principles of co-operative group intelligence and collective behavior of self-organized systems. This chapter presents key principles of multi-optimization, and swarm optimization for solving multi-objective engineering design problems with illustration through few examples.

Swarm Intelligence for Multi-Objective Optimization in Engineering Design

2019 ◽  
pp. 180-194
Author(s):  
Janga Reddy Manne
Keyword(s):  
Swarm Intelligence ◽  
Engineering Design ◽  
Random Search ◽  
Search Space ◽  
Population Based ◽  
Design Problems ◽  
Multi Objective ◽  
Hard Constraints ◽  
Decision Variables ◽  
Engineering Design Problems

Most of the engineering design problems are intrinsically complex and difficult to solve because of diverse solution search space, complex functions, continuous and discrete nature of decision variables, multiple objectives, and hard constraints. Swarm intelligence (SI) algorithms are becoming popular in dealing with these complexities. The SI algorithms, being population-based random search techniques, use heuristics inspired from nature to enable effective exploration of optimal solutions to complex engineering problems. The SI algorithms derived from principles of cooperative group intelligence and collective behavior of self-organized systems. This chapter presents key principles of multi-optimization and swarm optimization for solving multi-objective engineering design problems with illustration through a few examples.

scholarly journals An Improved Butterfly Optimization Algorithm for Engineering Design Problems Using the Cross-Entropy Method

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 1049 ◽  
Author(s):  
Guocheng Li ◽  
Fei Shuang ◽  
Pan Zhao ◽  
Chengyi Le
Keyword(s):  
Engineering Design ◽  
Optimization Algorithm ◽  
Global Search ◽  
Cross Entropy ◽  
Local Optimum ◽  
Test Functions ◽  
Design Problems ◽  
Cross Entropy Method ◽  
Engineering Design Problems ◽  
The Cross

Engineering design optimization in real life is a challenging global optimization problem, and many meta-heuristic algorithms have been proposed to obtain the global best solutions. An excellent meta-heuristic algorithm has two symmetric search capabilities: local search and global search. In this paper, an improved Butterfly Optimization Algorithm (BOA) is developed by embedding the cross-entropy (CE) method into the original BOA. Based on a co-evolution technique, this new method achieves a proper balance between exploration and exploitation to enhance its global search capability, and effectively avoid it falling into a local optimum. The performance of the proposed approach was evaluated on 19 well-known benchmark test functions and three classical engineering design problems. The results of the test functions show that the proposed algorithm can provide very competitive results in terms of improved exploration, local optima avoidance, exploitation, and convergence rate. The results of the engineering problems prove that the new approach is applicable to challenging problems with constrained and unknown search spaces.

Global Non-Iterative Design Optimization Using Monotonicity Analysis

1979 ◽  
Vol 101 (4) ◽  
pp. 645-649 ◽  
Author(s):  
P. Papalambros ◽  
D. J. Wilde
Keyword(s):  
Engineering Design ◽  
Numerical Solutions ◽  
Global Optimum ◽  
Numerical Techniques ◽  
Problem Statement ◽  
Design Problems ◽  
Iterative Design ◽  
Engineering Design Problems ◽  
Conventional Optimization ◽  
Monotonicity Analysis

The numerical techniques used in conventional optimization may not work for engineering design problems, which often present peculiar difficulties. Monotonicity analysis can identify a global optimum without any special computations and also prevent acceptance of false solutions generated by the numerical techniques. In the present paper, these advantages are demonstrated in a problem taken from the literature, for which diverse numerical solutions have been obtained. Monotonicity analysis identifies and proves the global optimum. Its application may require some algebraic rearrangement of the problem statement.

scholarly journals Cicada Swarm Optimization: A New Method for Optimizing Persistent Problems

2020 ◽  
Vol 13 (6) ◽  
pp. 279-293
Author(s):  
Hanan Akkar ◽  
◽  
Sameem Salman ◽  
Keyword(s):  
Engineering Design ◽  
Optimization Algorithm ◽  
Optimization Methods ◽  
Design Problems ◽  
Swarm Optimization ◽  
Engineering Design Problems ◽  
Grasshopper Optimization Algorithm ◽  
Minimum Number ◽  
Grasshopper Optimization ◽  
Design Pressure

This paper proposes a new meta-heuristic swarm optimization algorithm called Cicada Swarm Optimization (CISO) algorithm, which mimics the behavior of bio-inspired swarm optimization methods. The CISO algorithm is tested with 23 benchmark functions and taken two problems engineering design, pressure vessel problem and himmelblau’s problem. The performance of CISO algorithm is compared with meta-heuristic well-known and recently proposed algorithms (Cockroach Swarm Optimization (CSO), Grasshopper Optimization algorithm (GOA) and Particle Swarm Optimization (PSO)). The obtained results showed that the proposed algorithm succeeded in improving the test functions and solved engineering design problems that could not be improved by other algorithms according to the chosen parameters and the limits of the research space, also showed that CISO has a faster convergence with the minimum number of iterations and also have an accurate calculation efficiency Satisfactory compared to other optimization algorithms.

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