An Adaptive Hybrid PSO Multi-Objective Optimization Algorithm for Constrained Optimization Problems

2015 ◽  
Vol 29 (06) ◽  
pp. 1559009 ◽  
Author(s):  
Hongzhi Hu ◽  
Shulin Tian ◽  
Qing Guo ◽  
Aijia Ouyang
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Early Stage ◽  
Particle Swarm ◽  
Multi Objective Optimization ◽  
Constrained Optimization Problems ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Hybrid Particle Swarm Optimization

In attempting to overcome the limitation of current methods to solve complicated constrained optimization problems, this paper proposes an adaptive hybrid particle swarm optimization multi-objective optimization (AHPSOMO) algorithm. In the early stage, this algorithm initializes the individuals in a population in an even manner using good point set (GPS) theory so that the diversity of the population can be guaranteed. In the process of local search, differential evolution (DE) algorithm is introduced for updating local optimal individuals. Particle swarm optimization method is further adopted to conduct global search as per the multi-objective approach. The results of simulation tests on 24 classic test functions and three engineering constrained optimization problems show that compared with other algorithms, our proposed algorithm is effective and feasible, which can offer highly accurate solutions with good robustness.

Hybrid Immune Clonal Particle Swarm Optimization Multi-Objective Algorithm for Constrained Optimization Problems

2017 ◽  
Vol 31 (01) ◽  
pp. 1759001 ◽  
Author(s):  
Shengyu Pei
Keyword(s):  
Particle Swarm Optimization ◽  
Constrained Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Standard Test ◽  
Constrained Optimization Problems ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Local Optimal Solution

How to solve constrained optimization problems constitutes an important part of the research on optimization problems. In this paper, a hybrid immune clonal particle swarm optimization multi-objective algorithm is proposed to solve constrained optimization problems. In the proposed algorithm, the population is first initialized with the theory of good point set. Then, differential evolution is adopted to improve the local optimal solution of each particle, with immune clonal strategy incorporated to improve each particle. As a final step, sub-swarm is used to enhance the position and velocity of individual particle. The new algorithm has been tested on 24 standard test functions and three engineering optimization problems, whose results show that the new algorithm has good performance in both robustness and convergence.

A New Particle Swarm Optimization and Differential Evolution Technique for Constrained Optimization Problems

2013 ◽  
Author(s):  
Dhafar Al-Ani ◽  
Saeid Habibi
Keyword(s):  
Particle Swarm Optimization ◽  
Differential Evolution ◽  
Constrained Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Test Problems ◽  
Constrained Optimization Problems ◽  
Swarm Optimization ◽  
Multi Objective

Real-world problems are often complex and may need to deal with constrained optimization problems (COPs). This has led to a growing interest in optimization techniques that involve more than one objective function to be simultaneously optimized. Accordingly, at the end of the multi-objective optimization process, there will be more than one solution to be considered. This enables a trade-off of high-quality solutions and provides options to the decision-maker to choose a solution based on qualitative preferences. Particle Swarm Optimization (PSO) algorithms are increasingly being used to solve NP-hard and constrained optimization problems that involve multi-objective mathematical representations by finding accurate and robust solutions. PSOs are currently used in many real-world applications, including (but not limited to) medical diagnosis, image processing, speech recognition, chemical reactor, weather forecasting, system identification, reactive power control, stock exchange market, and economic power generation. In this paper, a new version of Multi-objective PSO and Differential Evolution (MOPSO-DE) is proposed to solve constrained optimization problems (COPs). As presented in this paper, the proposed MOPSO-DE scheme incorporates a new leader(s) updating mechanism that is invoked when the system is under the risk of converging to premature solutions, parallel islands mechanism, adaptive mutation, and then integrated to the DE in order to update the particles’ best position in the search-space. A series of experiments are conducted using 12 well-known benchmark test problems collected from the 2006 IEEE Congress on Evolutionary Computation (CEC2006) to verify the feasibility, performance, and effectiveness of the proposed MOPSO-DE algorithm. The simulation results show the proposed MOPSO-DE is highly competitive and is able to obtain the optimal solutions for the all test problems.

scholarly journals A Distributed Multifactorial Particle Swarm Optimization Approach

2021 ◽  
Author(s):  
Ahlem Aboud ◽  
Nizar Rokbani ◽  
Seyedali Mirjalili ◽  
Abdulrahman M. Qahtani ◽  
Omar Almutiry ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Beta Function ◽  
Particle Swarm ◽  
Search Space ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Swarm Optimization ◽  
Multi Objective ◽  
New System

<p>Multifactorial Optimization (MFO) and Evolutionary Transfer Optimization (ETO) are new optimization challenging paradigms for which the multi-Objective Particle Swarm Optimization system (MOPSO) may be interesting despite limitations. MOPSO has been widely used in static/dynamic multi-objective optimization problems, while its potentials for multi-task optimization are not completely unveiled. This paper proposes a new Distributed Multifactorial Particle Swarm Optimization algorithm (DMFPSO) for multi-task optimization. This new system has a distributed architecture on a set of sub-swarms that are dynamically constructed based on the number of optimization tasks affected by each particle skill factor. DMFPSO is designed to deal with the issues of handling convergence and diversity concepts separately. DMFPSO uses Beta function to provide two optimized profiles with a dynamic switching behaviour. The first profile, Beta-1, is used for the exploration which aims to explore the search space toward potential solutions, while the second Beta-2 function is used for convergence enhancement. This new system is tested on 36 benchmarks provided by the CEC’2021 Evolutionary Transfer Multi-Objective Optimization Competition. Comparatives with the state-of-the-art methods are done using the Inverted General Distance (IGD) and Mean Inverted General Distance (MIGD) metrics. Based on the MSS metric, this proposal has the best results on most tested problems.</p>

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