scholarly journals DMO-QPSO: A Multi-Objective Quantum-Behaved Particle Swarm Optimization Algorithm Based on Decomposition with Diversity Control

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1959
Author(s):  
Qi You ◽  
Jun Sun ◽  
Feng Pan ◽  
Vasile Palade ◽  
Bilal Ahmad
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithm ◽  
Particle Swarm ◽  
Test Problems ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Controlling Mechanism ◽  
Diversity Control ◽  
Polynomial Mutation ◽  
The Impact

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has shown remarkable effectiveness in solving multi-objective problems (MOPs). In this paper, we integrate the quantum-behaved particle swarm optimization (QPSO) algorithm with the MOEA/D framework in order to make the QPSO be able to solve MOPs effectively, with the advantage of the QPSO being fully used. We also employ a diversity controlling mechanism to avoid the premature convergence especially at the later stage of the search process, and thus further improve the performance of our proposed algorithm. In addition, we introduce a number of nondominated solutions to generate the global best for guiding other particles in the swarm. Experiments are conducted to compare the proposed algorithm, DMO-QPSO, with four multi-objective particle swarm optimization algorithms and one multi-objective evolutionary algorithm on 15 test functions, including both bi-objective and tri-objective problems. The results show that the performance of the proposed DMO-QPSO is better than other five algorithms in solving most of these test problems. Moreover, we further study the impact of two different decomposition approaches, i.e., the penalty-based boundary intersection (PBI) and Tchebycheff (TCH) approaches, as well as the polynomial mutation operator on the algorithmic performance of DMO-QPSO.

Multi-objective integrated planning and scheduling model for operating rooms under uncertainty

2018 ◽  
Vol 232 (9) ◽  
pp. 930-948 ◽  
Author(s):  
Javad Ansarifar ◽  
Reza Tavakkoli-Moghaddam ◽  
Faezeh Akhavizadegan ◽  
Saman Hassanzadeh Amin
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Particle Swarm Optimization ◽  
Heuristic Algorithms ◽  
Particle Swarm ◽  
Operating Rooms ◽  
Programming Approach ◽  
Test Problems ◽  
Swarm Optimization ◽  
Multi Objective

This article formulates the operating rooms considering several constraints of the real world, such as decision-making styles, multiple stages for surgeries, time windows for resources, and specialty and complexity of surgery. Based on planning, surgeries are assigned to the working days. Then, the scheduling part determines the sequence of surgeries per day. Moreover, an integrated fuzzy possibilistic–stochastic mathematical programming approach is applied to consider some sources of uncertainty, simultaneously. Net revenues of operating rooms are maximized through the first objective function. Minimizing a decision-making style inconsistency among human resources and maximizing utilization of operating rooms are considered as the second and third objectives, respectively. Two popular multi-objective meta-heuristic algorithms including Non-dominated Sorting Genetic Algorithm and Multi-Objective Particle Swarm Optimization are utilized for solving the developed model. Moreover, different comparison metrics are applied to compare the two proposed meta-heuristics. Several test problems based on the data obtained from a public hospital located in Iran are used to display the performance of the model. According to the results, Non-dominated Sorting Genetic Algorithm-II outperforms the Multi-Objective Particle Swarm Optimization algorithm in most of the utilized metrics. Moreover, the results indicate that our proposed model is more effective and efficient to schedule and plan surgeries and assign resources than manual scheduling.

scholarly journals Decision-Making of Underwater Cooperative Confrontation Based on MODPSO

Sensors ◽  
2019 ◽  
Vol 19 (9) ◽  
pp. 2211
Author(s):  
Na Wei ◽  
Mingyong Liu ◽  
Weibin Cheng
Keyword(s):  
Decision Making ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Communication Delay ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle ◽  
Multi Objective ◽  
Decision Making Model ◽  
The Impact

This paper proposes a multi-objective decision-making model for underwater countermeasures based on a multi-objective decision theory and solves it using the multi-objective discrete particle swarm optimization (MODPSO) algorithm. Existing decision-making models are based on fully allocated assignment without considering the weapon consumption and communication delay, which does not conform to the actual naval combat process. The minimum opponent residual threat probability and minimum own-weapon consumption are selected as two functions of the multi-objective decision-making model in this paper. Considering the impact of the communication delay, the multi-objective discrete particle swarm optimization (MODPSO) algorithm is proposed to obtain the optimal solution of the distribution scheme with different weapon consumptions. The algorithm adopts the natural number coding method, and the particle corresponds to the confrontation strategy. The simulation result shows that underwater communication delay impacts the decision-making selection. It verifies the effectiveness of the proposed model and the proposed multi-objective discrete particle swarm optimization algorithm.

Improved Multi-Objective Particle Swarm Optimization Algorithm for DNA Sequence Design

2020 ◽  
Vol 15 (12) ◽  
pp. 1450-1459
Author(s):  
Ying Niu ◽  
Hangyu Zhou ◽  
Shida Wang ◽  
Kai Zhao ◽  
Xiaoxiao Wang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Evolutionary Algorithm ◽  
Dna Sequence ◽  
Dna Sequences ◽  
Particle Swarm ◽  
Experimental Simulation ◽  
Swarm Optimization ◽  
Sequence Design ◽  
Multi Objective ◽  
Dna Sequence Design

The DNA sequence design is a vital step in reducing undesirable biochemical reactions and incorrect computations in successful DNA computing. To this end, many studies had concentrated on how to design higher quality DNA sequences. However, DNA sequences involve some thermodynamic and conflicting conditions, which in turn reflect the evolutionary algorithm process implemented through chemical reactions. In the present study, we applied an improved multi-objective particle swarm optimization (IMOPSO) algorithm to DNA sequence design, in which a chaotic map is combined with this algorithm to avoid falling into local optima. The experimental simulation and statistical results showed that the DNA sequence design method based on IMOPSO has higher reliability than the existing sequence design methods such as traditional evolutionary algorithm, invasive weed algorithm, and specialized methods.

scholarly journals Research on Outage Maintenance Optimization Strategy Based on Improved Multi-Objective Particle Swarm Optimization

2021 ◽  
Vol 2138 (1) ◽  
pp. 012008
Author(s):  
Cun Li ◽  
Ding Yuan ◽  
Gaige Liang ◽  
Lu Liu ◽  
Xuedong Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Power Grid ◽  
Complex Structure ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Large Power ◽  
Multi Objective ◽  
The Impact

Abstract With the increasingly complex structure of the transmission system, the impact of power outage caused by faults in the power grid is becoming greater and greater. The power grid shall cut off the maintenance of line equipment to ensure the power supply safety of the system. During the outage maintenance period, how to achieve safe maintenance and low-cost impact is the main content of outage control. Aiming at the problem of outage maintenance, a particle swarm optimization algorithm with the goal of minimum impact, economic optimization and shortest maintenance time is proposed. The game theory is used to select the optimal solution from the solutions of particle swarm optimization algorithm to solve the multi-objective function, so as to improve and ensure the population diversity, realize the efficiency and economy of outage maintenance, and meet the needs of outage plan of large power grid. The effectiveness of the proposed algorithm is further verified by comparing multi class baseline experiments.

Multi-objective robust design of vehicle structure based on multi-objective particle swarm optimization

2020 ◽  
Vol 39 (6) ◽  
pp. 9063-9071
Author(s):  
Haichao Liu ◽  
Xiangjie Jin ◽  
Fagui Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Optimization Method ◽  
Vehicle Body ◽  
Efficiency Coefficient ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Front Structure ◽  
Particle Swarm Optimization Method ◽  
The Impact

With the continuous spread of COVID-19 epidemic, the strict control of personnel makes it a problem to optimize the design of vehicle parameters after field measurement. The energy absorption characteristics and deformation mode of the front structure of the vehicle determine the acceleration or force response of the vehicle body during the impact, which plays an important role in occupant protection. The traditional multi-objective optimization method is to transform multi-objective problems into single objective optimization problems through weighted combination, objective planning, efficiency coefficient and other methods. This method requires a strong prior knowledge. The purpose of this paper is to combine the experimental design with the Multi-objective Particle Swarm Optimization (MPSO) method to achieve the optimization of the crash worthiness of automobile structure. This method can effectively overcome the defect of low precision caused by the conventional response surface method in the whole design space. In this paper, the multi-objective particle swarm optimization method is applied to the research of Crash worthiness optimization of automobile structure, which expands the application field of the multi-objective particle swarm optimization method, and also has a very big role in the optimization of other complex systems. It can be seen from the experiment that the speed of multi-objective particle swarm optimization is much faster than that of other methods. Only 100 iterations can get the relative better results. The case study on the front structure of a car shows that the method has a good result. It is of great significance to apply the method to the optimization design of the crash worthiness of the car structure to improve the crash safety of the car under the influence of COVID-19 epidemic.

Movement Strategies for Multi-Objective Particle Swarm Optimization

2010 ◽  
Vol 1 (3) ◽  
pp. 59-79 ◽  
Author(s):  
S. Nguyen ◽  
V. Kachitvichyanukul
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Space ◽  
Movement Behavior ◽  
Test Problems ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Movement Strategies

Particle Swarm Optimization (PSO) is one of the most effective metaheuristics algorithms, with many successful real-world applications. The reason for the success of PSO is the movement behavior, which allows the swarm to effectively explore the search space. Unfortunately, the original PSO algorithm is only suitable for single objective optimization problems. In this paper, three movement strategies are discussed for multi-objective PSO (MOPSO) and popular test problems are used to confirm their effectiveness. In addition, these algorithms are also applied to solve the engineering design and portfolio optimization problems. Results show that the algorithms are effective with both direct and indirect encoding schemes.

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.

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