A novel hybrid adaptive collaborative approach based on particle swarm optimization and local search for dynamic optimization problems

Sharifi, Ali; Kordestani, Javidan Kazemi; Mahdaviani, Mahshid; Meybodi, Mohammad Reza

doi:10.1016/j.asoc.2015.04.001

Cited by 33 publications

(13 citation statements)

References 48 publications

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“…A PSO-based memetic algorithm [142] uses a ring topology structure and a fuzzy cognition. A fuzzy cognition with multiple local searches was also used in [143]. An improved mQSO algorithm (mQSOE) [186] uses an evaporation mechanism as used in ACO to penalize the fitness value of the best position found by each particle in the past.…”

Section: Hybridization With Other Domain Knowledge For Exam-mentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

474

213

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Swarm intelligence (SI) algorithms, including ant colony optimization, particle swarm optimization, bee-inspired algorithms, bacterial foraging optimization, firefly algorithms, fish swarm optimization and many more, have been proven to be good methods to address difficult optimization problems under stationary environments. Most SI algorithms have been developed to address stationary optimization problems and hence, they can converge on the (near-) optimum solution efficiently. However, many real-world problems have a dynamic environment that changes over time. For such dynamic optimization problems (DOPs), it is difficult for a conventional SI algorithm to track the changing optimum once the algorithm has converged on a solution. In the last two decades, there has been a growing interest of addressing DOPs using SI algorithms due to their adaptation capabilities. This paper presents a broad review on SI dynamic optimization (SIDO) focused on several classes of problems, such as discrete, continuous, constrained, multi-objective and classification problems, and real-world applications. In addition, this paper focuses on the enhancement strategies integrated in SI algorithms to address dynamic changes, the performance measurements and benchmark generators used in SIDO. Finally, some considerations about future directions in the subject are given.

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Section: Hybridization With Other Domain Knowledge For Exam-mentioning

confidence: 99%

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Mavrovouniotis

Yang

2017

Swarm and Evolutionary Computation

474

213

View full text Add to dashboard Cite

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“…After the change is found, the memory cells will be activated for optima tracking in the upcoming environment. To effectively track the optima, a local search method called the Naïve directed sear algorithm [13] is adopted.…”

Section: Memory-based Optima Trackingmentioning

confidence: 99%

Innate-Adaptive Response and Memory based Artificial Immune System for Dynamic Optimization

Zhang¹,

Zhang²,

Zhang³

et al. 2018

IJPE

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Artificial immune systems (AIS) have been widely applied in optimization under static situations. Due to their dynamism, particular challenges are posed when handling dynamic optimization problems (DOPs). The designed algorithms must overcome these challenges to accomplish efficient results. In the paper, a new AIS based algorithm denoted as IAMAIS is proposed. In this algorithm, innate and adaptive responses in the immune system are elaborated on. The innate response is introduced to maintain the diversity of the population and implement global search, while the adaptive immune response is developed to locally locate the optima. Moreover, a memory mechanism is presented to reserve the found optima and further track the optima when environmental change happens. The experiments were applied on the most well-known benchmark, the Moving Peak Benchmark. Simulation results show that IAMAIS is competitive for Dops.

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“…Landscapes analysis is one of the fundamental approaches to understand the geometry of the search space that can provide important information for the development of a search algorithm [1]. The simple landscape measures such as the ruggedness, peak number, height, separation, and clustering in the solution space, can be used to reflect the changes in the landscapes for dynamic problems [8]. These landscape indicators can be a useful component in designing the algorithm for DOPs.…”

Section: Introductionmentioning

confidence: 99%

Landscape-Based Similarity Check Strategy for Dynamic Optimization Problems

Elsayed

Sarker

et al. 2020

IEEE Access

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In many real-world decision problems, there are specific objective, decision variables, conditions, data and/or parameters that may vary over time. When these problems are solved through an optimization process, they are generally known as dynamic optimization. Dynamic optimization is a challenging research topic as the optimal solution usually moves with any change in the problem environment. In locating the optimal point in any optimization problems, the effectiveness of the search process is influenced by the nature of the problem landscape. In order to improve the effectiveness of the search process, in this paper, a new approach is developed by integrating a landscape-based strategy with appropriately designed evolutionary algorithms for solving dynamic problems. The proposed approach is named as Landscape Influenced Dynamic Optimization Algorithm (LIDOA). LIDOA checks the similarity before and after the changed environment which is then used as an input to guide the evolutionary search process. The dynamic benchmark functions from IEEE-CEC2009 are solved using LIDOA. LIDOA was able to enhance the performance of the evolutionary algorithms in their adaptation to dynamic changes, which in turn enhanced their ability to attain better results.

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A novel hybrid adaptive collaborative approach based on particle swarm optimization and local search for dynamic optimization problems

Cited by 33 publications

References 48 publications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

A survey of swarm intelligence for dynamic optimization: Algorithms and applications

Innate-Adaptive Response and Memory based Artificial Immune System for Dynamic Optimization

Landscape-Based Similarity Check Strategy for Dynamic Optimization Problems

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