Complex continuous optimization problems widely exist nowadays due to the fast development of the economy and society. Moreover, the technologies like Internet of things, cloud computing, and big data also make optimization problems with more challenges including Many-dimensions, Many-changes, Many-optima, Many-constraints, and Many-costs. We term these as 5-M challenges that exist in large-scale optimization problems, dynamic optimization problems, multi-modal optimization problems, multi-objective optimization problems, many-objective optimization problems, constrained optimization problems, and expensive optimization problems in practical applications. The evolutionary computation (EC) algorithms are a kind of promising global optimization tools that have not only been widely applied for solving traditional optimization problems, but also have emerged booming research for solving the above-mentioned complex continuous optimization problems in recent years. In order to show how EC algorithms are promising and efficient in dealing with the 5-M complex challenges, this paper presents a comprehensive survey by proposing a novel taxonomy according to the function of the approaches, including reducing problem difficulty, increasing algorithm diversity, accelerating convergence speed, reducing running time, and extending application field. Moreover, some future research directions on using EC algorithms to solve complex continuous optimization problems are proposed and discussed. We believe that such a survey can draw attention, raise discussions, and inspire new ideas of EC research into complex continuous optimization problems and real-world applications.

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A survey on evolutionary computation for complex continuous optimization

Parameter adaptation-based ant colony optimization with dynamic hybrid mechanism

A novel wrapper-based feature subset selection method using modified binary differential evolution algorithm

Most research on improving differential evolution algorithms has focused on mutation operator and parameter control. In this paper, a new selection operator is proposed to improve differential evolution algorithm performance. When the individual is not in a state of stagnation, the proposed selection operator is the same as the classical selection operator, meaning that it chooses the best vector from the trial vector and parent vector to survive to the next generation. When the individual is in a state of stagnation, the three other candidate vectors may survive to the next generation. The first candidate vector is the best vector of all the discarded trial vectors of the parent vector. The second candidate vector is the second-best vector of all the discarded trial vectors of the parent vector. The third candidate vector is randomly chosen from all the successfully updated solutions. The proposed selection operator will improve the differential evolution algorithm’s ability to escape the local optimal value. 58 benchmark functions are used for verification of the proposed selection operator’s performance. Experiments were conducted in order to compare six differential evolution algorithms’ performances using the proposed selection operator and not using the proposed selection operator. Simulation results showed that the proposed selection operator significantly improved the differential evolution algorithm’s performance.

A new selection operator for differential evolution algorithm

A multi-stage evolutionary algorithm for multi-objective optimization with complex constraints

Multi-objective evolutionary clustering for large-scale dynamic community detection

A differential evolution based feature combination selection algorithm for high-dimensional data

Constraint satisfaction problem (CSP) is defined as a set of variables whose values need to satisfies a set of constraints. Ant colony optimization (ACO) has been proved to be a promising algorithm for solving the CSP, but the solution quality and convergence speed of existing ACO-based algorithms are not satisfactory. To overcome these drawbacks, this paper proposes an improved ant colony optimization with an automatic updating mechanism (AU-ACO). The idea of the automatic updating mechanism is to optimize an assignment without giving up the excellent variable-value pairs of the assignment. Under the impact of this mechanism, AU-ACO can only optimize the non-excellent variable-value pairs of a selected assignment, which results in the algorithm having a greater chance of finding better solutions. Furthermore, by optimizing only some variable-value pairs rather than all variable-value pairs, the convergence speed of the proposed algorithm is improved. AU-ACO is compared with eight other state-of-the-art algorithms on a wide range of binary problems, and experimental results demonstrate that AU-ACO a more effective and efficient for solving the CSP.

Yuhai Zhao

Papers

Multi-objective evolutionary clustering for large-scale dynamic community detection

A differential evolution based feature combination selection algorithm for high-dimensional data

An improved ant colony optimization with an automatic updating mechanism for constraint satisfaction problems