Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance

Sierra, Margarita Reyes; Coello, Carlos A. Coello

doi:10.1007/978-3-540-31880-4_35

Cited by 601 publications

(315 citation statements)

References 12 publications

Supporting

Mentioning

310

Contrasting

Unclassified

Order By: Relevance

“…[11,14,19]). MOEAs encompass a broad family of approaches to MOO; particularly successful among them are those based on particle swarm optimisation (PSO) [17,20,24], and others based on decomposing the MOO problem into several different single-objective problems [16,10]. In the former approach, PSO is combined with the use of measures to maintain a diverse set of 'targets', usually ensuring these are spread well across the developing Pareto set.…”

Section: Multi-objective Optimisationmentioning

confidence: 99%

Multi-Objective Probability Collectives

Waldock

Corne

2010

Applications of Evolutionary Computation

View full text Add to dashboard Cite

Abstract. We describe and evaluate a multi-objective optimisation (MOO) algorithm that works within the Probability Collectives (PC) optimisation framework. PC is an alternative approach to optimization where the optimization process focusses on finding an ideal distribution over the solution space rather than an ideal solution. We describe one way in which MOO can be done in the PC framework, via using a Pareto-based ranking strategy as a single objective. We partially evaluate this via testing on a number of problems, and compare the results with state of the art alternatives. We find that this first multi-objective probability collectives (MOPC) approach performs competitively, indicating both clear promise, and clear room for improvement.

show abstract

Section: Multi-objective Optimisationmentioning

confidence: 99%

Multi-Objective Probability Collectives

Waldock

Corne

2010

Applications of Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…The particle swarm optimization (PSO) algorithm is a population-based search algorithm based on the simulation of the social behavior of birds in a flock [30]. A particle is treated as a point in an n-dimension space, and the status of a particle is characterized by its position and velocity.…”

Section: 22mentioning

confidence: 99%

“…Time scales are set as in Figure 2, and solution representation is the AMK-list in [10,20], and [20,30], respectively. Update λ t∈ 15,20 = 2, λ t∈ 25,30 = 3.…”

Section: Complexitymentioning

confidence: 99%

Robust Optimization for Integrated Construction Scheduling and Multiscale Resource Allocation

Tao

Chong

et al. 2018

Complexity

View full text Add to dashboard Cite

This research investigates an integrated problem of construction scheduling and resource allocation. Inspired by complex construction practices, multi-time scale resources are considered for different length of terms, such as permanent staff and temporary workers. Differing from the common stochastic optimization problems, the resource price is supposed to be an uncertain parameter of which probability distribution is unknown, but observed data is given. Hence, the problem here is called Data-Driven Construction Scheduling and Multiscale Resource Allocation Problem (DD-CS&MRAP). Based on likelihood robust optimization, a multiobjective programming is developed where project completion time and expected resource cost are minimized simultaneously. To solve the problem efficiently, a double-layer metaheuristic comprised of Multiple Objective Particle Swarm Optimization (MOPSO) and interior point method named MOPSO-interior point algorithm is designed. The new solution presentation scheme and decoding process are developed. Finally, a construction case is used to validate the proposed method. The experimental results indicate that the MOPSO-interior point algorithm can reduce resource cost and improve the efficiency of resource utilization.

show abstract

“…PSO algorithms are simple and effective population-based stochastic techniques, which have their origin in the single objective optimization, but recently gained more popularity in the field of MOO. While several variants of multi-objective PSO have been developed [2,3,13,14], the basic algorithm is the same. A population of particles is initialized in an n-dimensional search space in which each particle x i = (x i,1 , ..., x i,n ) represents a (possible) solution.…”

Section: Particle Swarm Optimizationmentioning

confidence: 99%

Reference Point-Based Particle Swarm Optimization Using a Steady-State Approach

Allmendinger

Branke

2008

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Conventional multi-objective Particle Swarm Optimization (PSO) algorithms aim to find a representative set of Pareto-optimal solutions from which the user may choose preferred solutions. For this purpose, most multi-objective PSO algorithms employ computationally expensive comparison procedures such as non-dominated sorting. We propose a PSO algorithm, Reference point-based PSO using a SteadyState approach (RPSO-SS), that finds a preferred set of solutions near user-provided reference points, instead of the entire set of Pareto-optimal solutions. RPSO-SS uses simple replacement strategies within a steadystate environment. The efficacy of RPSO-SS in finding desired regions of solutions is illustrated using some well-known two and three-objective test problems.

show abstract

Improving PSO-Based Multi-objective Optimization Using Crowding, Mutation and ∈-Dominance

Cited by 601 publications

References 12 publications

Multi-Objective Probability Collectives

Multi-Objective Probability Collectives

Robust Optimization for Integrated Construction Scheduling and Multiscale Resource Allocation

Reference Point-Based Particle Swarm Optimization Using a Steady-State Approach

Contact Info

Product

Resources

About