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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Mahdavi, Sedigheh Rahnamayan, Shahryar Univ Ontario Inst Technol Dept Elect Comp & Software Engn Oshawa ON Canada

ISBN: (纸本)9781538627266

Differential Evolution (DE) is a population-based algorithm which has been successfully used to solve optimization problems. DE algorithm begins with an initial population with some randomly generated candidate solutions. During evolutionary process, the population of candidate solutions is evolved toward the promising region by using the specific operations. The population in the DE algorithm can resemble an especial perspective of a small society which has individuals to seek a common goal. In a society, the election system is commonly used as an effective approach;which is employed to determine one or several representatives who are responsible to make major decisions. Some machine learning algorithms are inspired from the society election system to develop an enhanced algorithm from a pool of potential algorithms with the complementary performances. This study is motivated from the election systems of societies which can be applied on population-based algorithms, here DE algorithm as a case study. We propose an election-based discrete DE algorithm which uses the information of all candidate solutions to create a new trial solution as a president candidate solution. During optimization phases, after applying the evolutionary operators, all candidate solutions vote to select the values of president's variables. In the proposed method, a majority voting method is applied to choose a value for each variable of the president candidate solution. We employ the discrete DE (DDE) algorithm as the parent algorithm to develop election-based discrete DE (EDDE) which is evaluated on the fifteen discrete benchmark functions. Simulation results confirm that EDDE obtains a promising performance on the majority of these functions.

关键词： Election Voting Evolutionary algorithms Differential Evolution population-based algorithms President

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Improving Time and Memory Efficiency of Genetic algorithms by Storing populations as Minimum Spanning Trees of Patches

Improving Time and Memory Efficiency of Genetic Algorithms b...

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Buzdalov, Maxim Aberystwyth Univ Aberystwyth Dyfed Wales

ISBN: (纸本)9798400701207

In many applications of evolutionary algorithms the computational cost of applying operators and storing populations is comparable to the cost of fitness evaluation. Furthermore, by knowing what exactly has changed in an individual by an operator, it is possible to recompute fitness value much more efficiently than from scratch. The associated time and memory improvements have been available for simple evolutionary algorithms, few specific genetic algorithms and in the context of gray-box optimization, but not for all algorithms, and the main reason is that it is difficult to achieve in algorithms using large arbitrarily structured populations. This paper makes a first step towards improving this situation. We show that storing the population as a minimum spanning tree, where vertices correspond to individuals but only contain meta-information about them, and edges store structural differences, or patches, between the individuals, is a viable alternative to the straightforward implementation. Our experiments suggest that significant, even asymptotic, improvements - including execution of crossover operators! - can be achieved in terms of both memory usage and computational costs.

关键词： genetic algorithms population-based algorithms minimum spanning tree performance

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An Improved Jaya Algorithm (IJAYA) for Optimization 8th

An Improved Jaya Algorithm (IJAYA) for Optimization

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8th International Conference on Soft Computing: Theories and Applications, SoCTA 2023

作者： Deshwal, Sonal Kumar, Pravesh Mogha, Sandeep Department of Mathematics Chandigarh University Punjab Mohali India Department of Mathematics Rajkiya Engineering College Bijnor Bijnor India

ISBN: (纸本)9789811998577

Jaya algorithm is a newly developed algorithm with a very straightforward structure and requires only a few number of control parameters for optimization. However, it needs to be improved in terms of exploration and accuracy of outcomes with a fast convergence speed. This work proposed an improved version "IJAYA", of the Jaya algorithm by introducing an average number-based equation of best and worst position of vectors in search space. The proposed method approaches worst and best positions to each other and resulting in increase in the search power and exploration index. IJAYA tested on six benchmark problems picked from the literature. Finally, the comparison and result demonstrate the effectiveness of our suggested approach. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Jaya algorithm Optimization population-based algorithms

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The novel combination lock algorithm for improving the performance of metaheuristic optimizers

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ADVANCES IN ENGINEERING SOFTWARE 2022年 172卷

作者： Bahreininejad, Ardeshir Taib, Hasnanizan Univ Teknol Brunei Fac Engn Gadong Brunei Univ Teknol Brunei Sch Appl Sci & Math Gadong Brunei

Nature-inspired population-based metaheuristics are promising search methods for solving optimization problems. In this paper, a novel systematic pre-processing approach, called the combination lock algorithm, for obtaining a good starting point for population-based algorithms is proposed. The proposed algorithm is tested on 32 benchmark unconstrained multidimensional optimization problems of different characteristics that are either unimodal or multimodal, continuous or non-continuous, separable or non-separable, differentiable or non-differentiable. The tests also include four engineering constrained optimization benchmark problems. The experimental results of applying the proposed algorithm for the Particle Swarm Optimization, the Ant Colony Optimization for Continuous Domain, and the Grey Wolf Optimization were compared with the results obtained from the conventional approach of initializing the starting population of population-based metaheuristic methods. The simulation results show the potential of the proposed algorithm as an efficient and reliable approach to enhance the performance of population-based optimization algorithms such that, overall, 50% and up to 100% of the tested problems "across various population size" settings, had either improved or equalled the optimal values when the proposed algorithm was applied.

关键词： population-based algorithms Optimization Combination lock algorithm Metaheuristic Constrained Unconstrained

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Metaheuristic-Boltzmannian optimization model: A new methodology for convergence using the Jensen-Shannon metric in continuous optimization problems

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SWARM AND EVOLUTIONARY COMPUTATION 2022年 75卷

作者： de Anda-Suarez, Juan Calzada-Ledesma, Valentin Ortiz-Aguilar, Lucero Tecnol Nacl Mexico Inst Tecnol Super Purisima Rincon Purisima Del Rincon 36425 Mexico

We present a new methodology called J-SIGMA to infer state change information using the minimum Jensen- Shannon distance to describe a continuous optimization problem as a Boltzmannian system, as well as its applications to population-based metaheuristics. In general, a Boltzmannian system describes a macrostate from the statistics of the constituent microstate where the minimum energy is the most probable. If we model an optimization problem as a Boltzmann process, the global optimum would be the most probable state. To achieve this, we propose an analytical derivation of the minimum distance to the Boltzmann distribution using the parameters and to fit this model. As a case study, we implemented the J-SIGMA methodology on three families of population-based metaheuristics: Swarm, Evolutionary, and Estimation of Distribution, and used a set of continuous optimization functions from CEC'17 to evaluate their performance against other metaheuristics of each family. Finally, from a statistical analysis of the convergence performance, the evidence is shown to affirm that the J-SIGMA methodology can significantly improve the convergence performance of the algorithms, regardless of the metaheuristic family to which it belongs.

关键词： Jensen-Shannon metric Boltzmann system population-based algorithms Particle Swarm Optimization Differential Evolution Estimation of distribution algorithms Continuous optimization problems

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A supervised parallel optimisation framework for metaheuristic algorithms

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 84卷

作者： Muttio, Eugenio J. Dettmer, Wulf G. Clarke, Jac Peric, Djordje Ren, Zhaoxin Fletcher, Lloyd Swansea Univ Fac Sci & Engn Bay CampusFabian Way Swansea SA1 8EN Wales UKAEA Culham Sci Ctr Abingdon OX14 3DB Oxon England

A Supervised Parallel Optimisation (SPO) is presented. The proposed framework couples different optimisation algorithms to solve single-objective optimisation problems. The supervision balances the exploration and exploitation capabilities of the distinct optimisers included, providing a general framework to solve problems with diverse characteristics. In this work, five optimisation algorithms are included in the ensemble: Particle Swarm Optimisation (PSO), Genetic Algorithm (GA), Covariance Matrix Adaption-Evolution Strategy (CMAES), Differential Evolution (DE), and Modified Cuckoo Search (MCS). A geometric path-finding problem with numerous local minima is used to demonstrate the advantage of SPO. The effectiveness of the approach is compared with that of stand-alone incidences of the integrated optimisation strategies and with state-of-the-art algorithms. In addition, a benchmark test suit composed of engineering applications is utilised to validate the general applicability of SPO with respect to a variety of problems. The good solutions generated by SPO are shown to be generally reproducible, while isolated algorithms, at best, render good solutions only occasionally.

关键词： Optimisation Parallel computation Metaheuristics population-based algorithms

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Clustered Routing Method in the Internet of Things Using a Moth-Flame Optimization Algorithm

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2021年第16期34卷 e4964-e4964页

作者： Sadrishojaei, Mahyar Navimipour, Nima Jafari Reshadi, Midia Hosseinzadeh, Mehdi Islamic Azad Univ Dept Comp Engn Sci & Res Branch Tehran Iran Islamic Azad Univ Dept Comp Engn Tabriz Branch Tabriz Iran Natl Yunlin Univ Sci & Technol Future Technol Res Ctr Touliu Yunlin Taiwan Iran Univ Med Sci Mental Hlth Res Ctr Psychosocial Hlth Res Inst Tehran Iran Univ Human Dev Comp Sci Sulaymaniyah Iraq

Internet of Things (IoT) is a set of interrelated devices on the Internet platform that can receive and send data to make human life more efficient and convenient. Clustering is a useful data collection method in the IoT that selectively cuts energy consumption by forming IoT nodes into some clusters. The cluster head can control all cluster nodes, and all intracluster and intercluster connections are made through it. Due to the NP-Hard nature of the clustering problem, a moth-flame optimization algorithm is proposed to select the minimum number of necessary clusters for routing in this article. This scheme is extracted from the life cycle of moths and enables effective communication by creating the optimal number of clusters. The proposed fitness function consists of the sum of the distances, the amount of energy remaining, and the degree of the nodes. The obtained experimental results are compared to different clustering algorithms, such as whale optimization algorithm, novel chemical reaction optimization, and cuckoo search optimization. The simulation using MATLAB clearly showed that the proposed method has a low number of cluster head nodes and the most balanced clusters. It also improves lifetime by at least 14.59% compared to the mentioned techniques.

关键词： clustering internet of things metaheuristic algorithm moth-flame optimization population-based algorithms routing

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A particle swarm inspired approach for continuous distributed constraint optimization problems

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年第PartB期123卷

作者： Choudhury, Moumita Sarker, Amit Yaser, Samin Khan, Md. Maruf Al Alif Yeoh, William Khan, Md. Mosaddek Univ Massachusetts Amherst Coll Informat & Comp Sci Amherst MA USA Washington Univ St Louis Dept Comp Sci & Engn 1 Brookings Dr St Louis MO 63130 USA Univ Dhaka Dept Comp Sci & Engn Sci Complex Dhaka 1000 Bangladesh

Distributed Constraint Optimization Problems (DCOPs) are a widely studied framework for coordinating in-teractions in cooperative multi-agent systems. In classical DCOPs, variables owned by agents are assumed to be discrete. However, in many applications, such as target tracking or sleep scheduling in sensor networks, continuous-valued variables are more suitable than discrete ones. To better model such applications, researchers have proposed Continuous DCOPs (C-DCOPs), an extension of DCOPs, that can explicitly model problems with continuous variables. The state-of-the-art approaches for solving C-DCOPs experience either onerous memory or computation overhead and are unsuitable for non-differentiable optimization problems. To address this issue, we propose a new C-DCOP algorithm, namely Particle Swarm Optimization based C-DCOP (PCD), which is inspired by Particle Swarm Optimization (PSO), a well-known centralized population -based approach for solving continuous optimization problems. In recent years, population-based algorithms have gained significant attention in classical DCOPs due to their ability in producing high-quality solutions. Nonetheless, to the best of our knowledge, this class of algorithms has not been utilized to solve C-DCOPs and there has been no work evaluating the potential of PSO in solving classical DCOPs or C-DCOPs. In light of this observation, we adapted PSO, a centralized algorithm, to solve C-DCOPs in a decentralized manner. The resulting PCD algorithm not only produces good-quality solutions but also finds solution without any requirement for derivative calculations. Moreover, we design a crossover operator that can be used by PCD to further improve the quality of solutions found. Finally, we theoretically prove that PCD is an anytime algorithm and empirically evaluate PCD against the state-of-the-art C-DCOP algorithms in a wide variety of benchmarks.

关键词： population-based algorithms Distributed problem solving DCOPs Multi-agent systems

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A memory guided sine cosine algorithm for global optimization

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2020年 93卷 103718-103718页

作者： Gupta, Shubham Deep, Kusum Engelbrecht, Andries P. Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttarakhand India Stellenbosch Univ Dept Ind Engn Stellenbosch South Africa Stellenbosch Univ Comp Sci Div Stellenbosch South Africa

Real-world optimization problems demand an algorithm which properly explores the search space to find a good solution to the problem. The sine cosine algorithm (SCA) is a recently developed and efficient optimization algorithm, which performs searches using the trigonometric functions sine and cosine. These trigonometric functions help in exploring the search space to find an optimum. However, in some cases, SCA becomes trapped in a sub-optimal solution due to an inefficient balance between exploration and exploitation. Therefore, in the present work, a balanced and explorative search guidance is introduced in SCA for candidate solutions by proposing a novel algorithm called the memory guided sine cosine algorithm (MG-SCA). In MG-SCA, the number of guides is decreased with increase in the number of iterations to provide a sufficient balance between exploration and exploitation. The performance of the proposed MG-SCA is analysed on benchmark sets of classical test problems, IEEE CEC 2014 problems, and four well known engineering benchmark problems. The results on these applications demonstrate the competitive ability of the proposed algorithm as compared to other algorithms.

关键词： Optimization population-based algorithms Sine cosine algorithm Exploration-exploitation

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A survey on dynamic populations in bio-inspired algorithms

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2024年第2期25卷 19-19页

作者： Farinati, Davide Vanneschi, Leonardo Univ Nova Lisboa NOVA Informat Management Sch NOVA IMS Campus Campolide P-1070312 Lisbon Portugal

population-based Bio-Inspired algorithms (PBBIAs) are computational methods that simulate natural biological processes, such as evolution or social behaviors, to solve optimization problems. Traditionally, PBBIAs use a population of static size, set beforehand through a specific parameter. Nevertheless, for several decades now, the idea of employing populations of dynamic size, capable of adjusting during the course of a single run, has gained ground. Various methods have been introduced, ranging from simpler ones that use a predefined function to determine the population size variation, to more sophisticated methods where the population size in different phases of the evolutionary process depends on the dynamics of the evolution itself and events occurring within the population during the run. The common underlying idea in many of these approaches, is similar: to save a significant amount of computational effort in phases where the evolution is functioning well, and therefore a large population is not needed. This allows for reusing the previously saved computational effort when optimization becomes more challenging, and hence a greater computational effort is required. Numerous past contributions have demonstrated a notable advantage of using dynamically sized populations, often resulting in comparable results to those obtained by the standard PBBIAs but with a significant saving of computational effort. However, despite the numerous successes that have been presented, to date, there is still no comprehensive collection of past contributions on the use of dynamic populations that allows for their categorization and critical analysis. This article aims to bridge this gap by presenting a systematic literature review regarding the use of dynamic populations in PBBIAs, as well as identifying gaps in the research that can lead the path to future works.

关键词： population-based algorithms Bio-inspired algorithms population size Dynamic population Adaptive population

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