Evaluating selection methods on hyper-heuristic multi-objective particle swarm optimization

Castro, Olacir R.; Fritsche, Gian; Pozo, Aurora

doi:10.1007/s10732-018-9369-x

Cited by 14 publications

(9 citation statements)

References 49 publications

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“…Many multi-objective particle swarm optimization (MOPSO) algorithms have been proposed to solve the problem of multi-objective optimization in the literature [13] and [37][38][39][40]. Many MOPSOs algorithms concentrate on procedures for choosing the best global, and some MOPSOs use a fixed value for dynamics and accelerations.…”

Section: Multi-objective Particle Swarm Optimizationmentioning

confidence: 99%

“…Particle Swarm Optimization (PSO), which is an effective metaheuristic optimization method, stimulates the social behavior of the animals [27]. For many challenging optimization problems, it performs better than the other optimization methods at quicker and more robust rates of convergence [28][29][30][31][32][33][34][35][36][37][38][39][40]. Bonyadi and Michalewicz [28] reported a comprehensive study of limitations, modifications and applications of PSO types for optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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Untitled

2020

Adv. Electr. Comp. Eng.

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One of the vital optimization issues in energy systems is the problem of economic load dispatch (ED). On the other hand, solar, wind, and other renewable energies are important energy sources for reducing hazardous emissions. This paper suggests an improved multi-objective particle swarm optimization algorithm (IMOPSO) that uses a functional inertial weight and a functional constriction factor to solve the multi-objective environmental-economic load dispatch (MEED) problem. A mutation strategy is used in IMOPSO, and a mutation operator, which is implemented for each particle in the swarm, is used to find optimum Pareto fronts. In this paper, the proposed IMOPSO is applied to the MEED problem under consideration of emission pollution, wind energy, prohibited operating zone, ramp limits, valve point effects, and transmission losses. The proposed technique is tested on the IEEE 30-bus, the IEEE 118-bus test system, and the modified IEEE 118-bus test system with emission coefficients, ramp rate limits, wind power, and prohibited operating zone. The IMOPSOs are compared with the results of various multi-objective algorithms to solve the MEED problem. The simulation results indicate that the IMOPSO produces better results than the compared multi-objective optimization algorithms for various test systems.

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Section: Multi-objective Particle Swarm Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Untitled

2020

Adv. Electr. Comp. Eng.

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“…In their MOHH, HLH selects suitable operators through a 'reward' mechanism. Castro et al [59] integrated HH into MOPSO to select a proper combination of leader and archiving methods, and four selection strategies (i.e., CF, MAB, SR and roulette wheel) were designed. Zhang et al [60] applied extreme value credit to reward operators and probability matching to select suitable operators.…”

Section: Hyper-heuristic Reviewmentioning

confidence: 99%

A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints

et al. 2019

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With the aim of reducing cost, carbon emissions, and service periods and improving clients’ satisfaction with the logistics network, this paper investigates the optimization of a variant of the location-routing problem (LRP), namely the regional low-carbon LRP (RLCLRP), considering simultaneous pickup and delivery, hard time windows, and a heterogeneous fleet. In order to solve this problem, we construct a biobjective model for the RLCLRP with minimum total cost consisting of depot, vehicle rental, fuel consumption, carbon emission costs, and vehicle waiting time. This paper further proposes a novel hyper-heuristic (HH) method to tackle the biobjective model. The presented method applies a quantum-based approach as a high-level selection strategy and the great deluge, late acceptance, and environmental selection as the acceptance criteria. We examine the superior efficiency of the proposed approach and model by conducting numerical experiments using different instances. Additionally, several managerial insights are provided for logistics enterprises to plan and design a distribution network by extensively analyzing the effects of various domain parameters such as depot cost and location, client distribution, and fleet composition on key performance indicators including fuel consumption, carbon emissions, logistics costs, and travel distance and time.

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“…Based on the development of MOHHs in Table 2, LLHs can be operators and metaheuristics, and thus two main modules can be obtained: (1) MOEA-based hyperheuristic (MOHH-I) which utilize operators (i.e., crossover, mutation, and domain-specific operators) or components (such as leader selection methods and archiving strategies in MOEAs [65,67,68,79]) as LLHs and corresponding elitism selection strategies, such as NSGA-II ranking mechanism [54,57,59,60,74,78,81,82] and Pareto Strength (SPEA2) [57,78], as acceptance criteria. Here, HLHs can access the objective value [60] and the methods involving objective space instead of solution space are independent of the domain problem.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Here, HLHs can access the objective value [60] and the methods involving objective space instead of solution space are independent of the domain problem. (2) Basic MOHH-II, which use MOEAs as LLHs, e.g., Li et al [60,83] and Maashi et al [61,64], and design corresponding acceptance criterion, such as AM [61,64], improving and equal [67,68,79], GDA and LA [64,83]. However, this classification is not shared by other studies.…”

Section: Literature Reviewmentioning

confidence: 99%

An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem

Leng

Zhao

Zhang

et al. 2019

IJERPH

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In this paper, we consider a variant of the location-routing problem (LRP), namely the the multiobjective regional low-carbon LRP (MORLCLRP). The MORLCLRP seeks to minimize service duration, client waiting time, and total costs, which includes carbon emission costs and total depot, vehicle, and travelling costs with respect to fuel consumption, and considers three practical constraints: simultaneous pickup and delivery, heterogeneous fleet, and hard time windows. We formulated a multiobjective mixed integer programming formulations for the problem under study. Due to the complexity of the proposed problem, a general framework, named the multiobjective hyper-heuristic approach (MOHH), was applied for obtaining Pareto-optimal solutions. Aiming at improving the performance of the proposed approach, four selection strategies and three acceptance criteria were developed as the high-level heuristic (HLH), and three multiobjective evolutionary algorithms (MOEAs) were designed as the low-level heuristics (LLHs). The performance of the proposed approach was tested for a set of different instances and comparative analyses were also conducted against eight domain-tailored MOEAs. The results showed that the proposed algorithm produced a high-quality Pareto set for most instances. Additionally, extensive analyses were also carried out to empirically assess the effects of domain-specific parameters (i.e., fleet composition, client and depot distribution, and zones area) on key performance indicators (i.e., hypervolume, inverted generated distance, and ratio of nondominated individuals). Several management insights are provided by analyzing the Pareto solutions.

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Evaluating selection methods on hyper-heuristic multi-objective particle swarm optimization

Cited by 14 publications

References 49 publications

Untitled

Untitled

A Novel Hyper-Heuristic for the Biobjective Regional Low-Carbon Location-Routing Problem with Multiple Constraints

An Effective Approach for the Multiobjective Regional Low-Carbon Location-Routing Problem

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