scholarly journals A survey on evolutionary computation for complex continuous optimization

2021 ◽  
Author(s):  
Zhi-Hui Zhan ◽  
Lin Shi ◽  
Kay Chen Tan ◽  
Jun Zhang
Keyword(s):  
Evolutionary Computation ◽  
Large Scale ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Future Research ◽  
Practical Applications ◽  
Fast Development ◽  
Comprehensive Survey ◽  
Continuous Optimization Problems ◽  
Expensive Optimization

AbstractComplex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

scholarly journals Tree-Seed Algorithm for Large-Scale Binary Optimization

2018 ◽  
Vol 3 (1) ◽  
pp. 48 ◽  
Author(s):  
Ahmet Cevahir Cinar ◽  
Hazim Iscan ◽  
Mustafa Servet Kiran
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Logic Gate ◽  
Continuous Optimization ◽  
Population Based ◽  
Benchmark Problems ◽  
Binary Optimization ◽  
Continuous Optimization Problems ◽  
Computation Algorithms ◽  
Tree Seed

Population-based swarm or evolutionary computation algorithms in optimization are attracted the interest of the researchers due their simple structure, optimization performance, easy-adaptation. Binary optimization problems can be also solved by using these algorithms. This paper focuses on solving large scale binary optimization problems by using Tree-Seed Algorithm (TSA) proposed for solving continuous optimization problems by imitating relationship between the trees and their seeds in nature. The basic TSA is modified by using xor logic gate for solving binary optimization problems in this study. In order to investigate the performance of the proposed algorithm, the numeric benchmark problems with the different dimensions are considered and obtained results show that the proposed algorithm produces effective and comparable solutions in terms of solution quality.Keywords: binary optimization, tree-seed algorithm, xor-gate, large-scale optimization

Evolutionary Large-Scale Multi-Objective Optimization: A Survey

2022 ◽  
Vol 54 (8) ◽  
pp. 1-34
Author(s):  
Ye Tian ◽  
Langchun Si ◽  
Xingyi Zhang ◽  
Ran Cheng ◽  
Cheng He ◽  
...  
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Benchmark Problems ◽  
Future Research ◽  
Decision Variable ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Space Reduction ◽  
Decision Variables ◽  
Comprehensive Survey

Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization.

Hybrid Genetic Algorithm

2014 ◽  
pp. 268-305 ◽  
Author(s):  
Kedar Nath Das
Keyword(s):  
Optimization Problems ◽  
Hybrid Genetic Algorithm ◽  
Continuous Optimization ◽  
Benchmark Problems ◽  
Future Research ◽  
Test Bed ◽  
Unconstrained Optimization Problems ◽  
Power Mutation ◽  
Continuous Optimization Problems ◽  
Real Coded Genetic Algorithms

Real coded Genetic Algorithms (GAs) are the most effective and popular techniques for solving continuous optimization problems. In the recent past, researchers used the Laplace Crossover (LX) and Power Mutation (PM) in the GA cycle (namely LX-PM) efficiently for solving both constrained and unconstrained optimization problems. In this chapter, a local search technique, namely Quadratic Approximation (QA) is discussed. QA is hybridized with LX-PM in order to improve its efficiency and efficacy. The generated hybrid system is named H-LX-PM. The supremacy of H-LX-PM over LX-PM is validated through a test bed of 22 unconstrained and 15 constrained typical benchmark problems. In the later part of this chapter, a few applications of GA in networking optimization are highlighted as the scope for future research.

scholarly journals A Novel Monarch Butterfly Optimization with Global Position Updating Operator for Large-Scale 0-1 Knapsack Problems

Mathematics ◽  
2019 ◽  
Vol 7 (11) ◽  
pp. 1056 ◽  
Author(s):  
Feng ◽  
Yu ◽  
Wang
Keyword(s):  
Large Scale ◽  
Optimization Problems ◽  
Orthogonal Design ◽  
Continuous Optimization ◽  
Monarch Butterfly ◽  
Migration Behavior ◽  
Monarch Butterflies ◽  
Monarch Butterfly Optimization ◽  
Low Dimensional ◽  
Continuous Optimization Problems

As a significant subset of the family of discrete optimization problems, the 0-1 knapsack problem (0-1 KP) has received considerable attention among the relevant researchers. The monarch butterfly optimization (MBO) is a recent metaheuristic algorithm inspired by the migration behavior of monarch butterflies. The original MBO is proposed to solve continuous optimization problems. This paper presents a novel monarch butterfly optimization with a global position updating operator (GMBO), which can address 0-1 KP known as an NP-complete problem. The global position updating operator is incorporated to help all the monarch butterflies rapidly move towards the global best position. Moreover, a dichotomy encoding scheme is adopted to represent monarch butterflies for solving 0-1 KP. In addition, a specific two-stage repair operator is used to repair the infeasible solutions and further optimize the feasible solutions. Finally, Orthogonal Design (OD) is employed in order to find the most suitable parameters. Two sets of low-dimensional 0-1 KP instances and three kinds of 15 high-dimensional 0-1 KP instances are used to verify the ability of the proposed GMBO. An extensive comparative study of GMBO with five classical and two state-of-the-art algorithms is carried out. The experimental results clearly indicate that GMBO can achieve better solutions on almost all the 0-1 KP instances and significantly outperforms the rest.

Multi-Objective Reliability-Based Optimization with Stochastic Metamodels

2011 ◽  
Vol 19 (4) ◽  
pp. 525-560 ◽  
Author(s):  
Rajan Filomeno Coelho ◽  
Philippe Bouillard
Keyword(s):  
Structural Engineering ◽  
Large Scale ◽  
Optimization Problems ◽  
Probability Distributions ◽  
Computational Cost ◽  
Continuous Optimization ◽  
Computational Effort ◽  
Kriging Interpolation ◽  
Simulation Code ◽  
Continuous Optimization Problems

This paper addresses continuous optimization problems with multiple objectives and parameter uncertainty defined by probability distributions. First, a reliability-based formulation is proposed, defining the nondeterministic Pareto set as the minimal solutions such that user-defined probabilities of nondominance and constraint satisfaction are guaranteed. The formulation can be incorporated with minor modifications in a multiobjective evolutionary algorithm (here: the nondominated sorting genetic algorithm-II). Then, in the perspective of applying the method to large-scale structural engineering problems—for which the computational effort devoted to the optimization algorithm itself is negligible in comparison with the simulation—the second part of the study is concerned with the need to reduce the number of function evaluations while avoiding modification of the simulation code. Therefore, nonintrusive stochastic metamodels are developed in two steps. First, for a given sampling of the deterministic variables, a preliminary decomposition of the random responses (objectives and constraints) is performed through polynomial chaos expansion (PCE), allowing a representation of the responses by a limited set of coefficients. Then, a metamodel is carried out by kriging interpolation of the PCE coefficients with respect to the deterministic variables. The method has been tested successfully on seven analytical test cases and on the 10-bar truss benchmark, demonstrating the potential of the proposed approach to provide reliability-based Pareto solutions at a reasonable computational cost.

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