In multiobjective optimization problems, there may exist two or more distinct Pareto optimal sets
(PSs) corresponding to the same Pareto Front (PF). These problems are defined as multimodal
multiobjective optimization problems (MMOPs). Arguably, finding one of these multiple PSs may be
sufficient to obtain an acceptable solution for some problems. However, failing to identify more
than one of the PSs may prevent the decision maker from considering solution options that could
bring about improved performance.
The aim of this special session is to promote the research on MMO and hence motivate researchers to formulate real-world practical problems. Given that the study of multimodal multiobjective optimization (MMO) is still in its emerging stages, although many real-word applications are likely to be amenable to treatment as a MMOP, to date the researchers have ignored such formulations:
This special session is devoted to the novel approaches, algorithms and techniques for solving MMOPs. The main topics of the special session are:
Papers should be submitted following the instructions at the IEEE CEC 2019 web site. Please select the main research topic as the Special Session on “multimodal multiobjective optimization”. Accepted papers will be included and published in the conference proceedings.
7th January 2019, 23:59 (GMT)
Supported by IEEE CIS Task Force from Intelligent Systems Applications Technical Committee, Task Force on "Transfer Learning & Transfer Optimization"
The human possesses the most remarkable ability to manage and execute multiple tasks simultaneously,
e.g., talking while walking. This desirable multitasking capability has inspired computational
methodologies and approaches to tackle multiple tasks at the same time by leveraging commonalities
and differences across different tasks to improve the performance and efficiency of resolving
component tasks compared to when dealing with them separately. As a well-known example, multi-task
learning is a very active subfield of machine learning whereby multiple learning tasks are
performed together using a shared model representation such that the relevant information contained
in related tasks can be exploited to improve the learning efficiency and generalization performance
of task-specific models.
Multi-task optimization (MTO) is a newly emerging research area in the field of optimization, which investigates how to effectively and efficiently tackle multiple optimization problems at the same time. In the multitasking scenario, solving one optimization problem may assist in solving other optimization problems (i.e., synergetic problem-solving) if these problems bear commonality and/or complementarity in terms of optimal solutions and/or fitness landscapes. As a simple example, if some problems have the same globally optimal solution but distinct fitness landscapes, obtaining the global optimum to any problem makes the others also get solved. Recently, an evolutionary MTO paradigm named as evolutionary multitasking was proposed to explore the potential of evolutionary algorithms (EAs) incorporated with a unified solution representation space for MTO. As a population-based optimizer, EAs feature the Darwinian “survival-of-the-fittest” principle and nature-inspired reproduction operations which inherently promote implicit knowledge transfer across tasks during problem-solving. The superiority of this new evolutionary multitasking framework over traditional ways of solving each task independently has been demonstrated on synthetic and real-world MTO problems by using a multi-factorial EA (MFEA) developed under this framework.
Evolutionary multitasking opens up new horizons for researchers in the field of evolutionary computation. It provides a promising means to deal with the ever-increasing number, variety and complexity of optimization tasks. More importantly, rapid advances in cloud computing could eventually turn optimization into an on-demand service hosted on the cloud. In such a case, a variety of optimization tasks would be simultaneously executed by the service engine where evolutionary multitasking may harness the underlying synergy between multiple tasks to provide service consumers with faster and better solutions.
Due to the good response of this competition held at CEC’17 and WCCI’2018 (17 entries in CEC’17, and 13 entries in WCCI’18), we would like to continue to organize this competition at CEC’19, aiming at promoting research advances in both algorithmic and theoretical aspects of evolutionary MTO.
Please refer to the complete document for more details.
Interested participants are strongly encouraged to report their approaches and results in a paper and submit it to "CEC-01 Special Session on Memetic Computing" before the CEC 2019 paper submission deadline If you would like to participate in the competition, please kindly inform us about your interest via email (firstname.lastname@example.org) so that we can update you about any bug fixings and/or the extension of the deadline.
7th January 2019, 23:59 (GMT)
Supported by IEEE CIS TF on “Intelligence Systems for Health” in the Intelligent Systems Application Technical Committee and IEEE CIS TF on “Data-Driven Evolutionary Optimization of Expensive Problems” in the Evolutionary Computation Technical Committee
Evolutionary multi-objective optimization (EMO) has been flourishing for two decades in academia. However, the industry applications of EMO to real-world optimization problems are infrequent, due to the strong assumption that objective function evaluations are easily accessed. In fact, such objective functions may not exist, instead computationally expensive numerical simulations or costly physical experiments must be performed for evaluations. Such problems driven by data collected in simulations or experiments are formulated as data-driven optimization problems, which pose challenges to conventional EMO algorithms. Firstly, obtaining the minimum data for conventional EMO algorithms to converge requires a high computational or resource cost. Secondly, although surrogate models that approximate objective functions can be used to replace the real function evaluations, the search accuracy cannot be guaranteed because of the approximation errors of surrogate models. Thirdly, since only a small amount of online data is allowed to be sampled during the optimization process, the management of online data significantly affects the performance of algorithms. The research on data-driven evolutionary optimization has not received sufficient attention, although techniques for solving such problems are highly in demand. One main reason is the lack of benchmark problems that can closely reflect real-world challenges, which leads to a big gap between academia and industries.
In this competition, we carefully select 6 benchmark multi-objective optimization problems from real-world applications, including design of car cab, optimization of vehicle frontal structure, filter design, optimization of power systems, and optimization of neural networks. The objective functions of those problems cannot be calculated analytically, but can be calculated by calling an executable program to provide true black-box evaluations for both offline and online data sampling. A set of initial data is generated offline using Latin hypercube sampling, and a predefined fixed number of online data samples are set as the stopping criterion. This competition, as an event organized by the Task Force on “Intelligence Systems for Health” in the Intelligent Systems Application Technical Committee and Task Force on “Data-Driven Evolutionary Optimization of Expensive Problems” in the Evolutionary Computation Technical Committee, aims to promote the research on data-driven evolutionary multi-objective optimization by suggesting a set of benchmark problems extracted from various real-world optimization applications. All benchmark functions are implemented in MATLAB code. Also, the MATLAB code has been embedded in a recently developed software platform – PlatEMO, an open source MATLAB-based platform for evolutionary multi- and many-objective optimization, which currently includes more than 50 representative algorithms and over 100 benchmark functions, along with a variety of widely used performance indicators.
15th April 2019, 23:59 (GMT)
To shape a low carbon energy future has been a crucial and urgent task under Paris Global Agreement.
Numerous optimisation problems have been formulated and solved to effectively save the fossil fuel
cost and relief energy waste from power system and energy application side. However, some key
problems are of strong non-convex, non-smooth or mixed integer characteristics, leading to
significant challenging issues for system operators and energy users. This competition aims to
encourage the relevant researchers to present their state-of-the-art optimisation tools for solving
three featured complicated optimisation tasks including unit commitment, economic load dispatch and
parameter identification for photovoltaic models and PEV fuel cells.
Unit commitment (UC) problem aims to minimize the economic cost by optimally determining the online/offline status and power dispatch of each unit, while maintaining various system constraints, formulating a large scale mixed-integer problem. Economic load dispatch is a power system operation task aiming to minimise the fossil fuel economic cost by determining the day-ahead and/or hourly power generation for each power generator. Fuel cell is one of most important energy storages in the future, particularly with the applications to vehicles and robotics. Proton Exchange Membrane is the key component of fuel cell however is of significant difficulties to be accurately modelled due to the nonlinearity, multivariate and strongly coupled characteristics. Evolutionary computation is immune from complex problem modelling formulation, and is therefore promising to provide powerful optimisation tools for intelligently and efficiently solving problems such as smart grid and various energy systems scheduling to reduce carbon consumptions.
A brief list of potential submission topics is shown below:
This competition intends to reflect the state-of-the-art advances of evolutionary optimisation approaches for solving emerging problems in complex modern power and energy system. In this competition, we choose the above three questions as the optimization object, in order to make it easier for comparative studies of different algorithms using the same platform, and get the better optimization results. The simulate experiment and data should be expressed on MATLAB platforms or other software platforms, therefore be ranked by the results according to the competition evaluation criteria. Interested participants are strongly encouraged to report their approaches and results in a paper and submit it to our special session CEC-17 Special Session on Evolutionary Computations on Smart Grid and Sustainable Energy Systems in the conference submission system, and also send their codes to the competition organizer at email@example.com for verification. All the papers should be submitted before the conference paper submission deadline.
7th Jan 2019, 23:59 (GMT)
Following the success of the previous edition at WCCI 2018, we are relaunching this competition at major
conferences in the field of computational
intelligence. This CEC 2019 competition proposes optimization of a centralized day-ahead energy
resource management problem in smart grids under environments with uncertainty. This year we
increased the difficulty by proving a more challenging case study, namely with higher degree of
The CEC 2019 competition on “Evolutionary Computation in Uncertain Environments: A Smart Grid Application” has the purpose of bringing together and testing the more advanced Computational Intelligence (CI) techniques applied to an energy domain problem, namely the energy resource management problem under uncertain environments. The competition provides a coherent framework where participants and practitioners of CI can test their algorithms to solve a real-world optimization problem in the energy domain with uncertainty consideration, which makes the problem more challenging and worth to explore.
Jan 7th, 23:59 (GMT) (For those submitting papers to the special session)
April 30th, 29:59 (GMT) (Submission without paper)