  Numerous multi-objective evolutionary algorithms have been designed for
constrained optimisation over past two decades. The idea behind these
algorithms is to transform constrained optimisation problems into
multi-objective optimisation problems without any constraint, and then solve
them. In this paper, we propose a new multi-objective method for constrained
optimisation, which works by converting a constrained optimisation problem into
a problem with helper and equivalent objectives. An equivalent objective means
that its optimal solution set is the same as that to the constrained problem
but a helper objective does not. Then this multi-objective optimisation problem
is decomposed into a group of sub-problems using the weighted sum approach.
Weights are dynamically adjusted so that each subproblem eventually tends to a
problem with an equivalent objective. We theoretically analyse the computation
time of the helper and equivalent objective method on a hard problem called
``wide gap''. In a ``wide gap'' problem, an algorithm needs exponential time to
cross between two fitness levels (a wide gap). We prove that using helper and
equivalent objectives can shorten the time of crossing the ``wide gap''. We
conduct a case study for validating our method. An algorithm with helper and
equivalent objectives is implemented. Experimental results show that its
overall performance is ranked first when compared with other eight state-of-art
evolutionary algorithms on IEEE CEC2017 benchmarks in constrained optimisation.
